基于粒子滤波的视频目标跟踪算法的研究
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摘要
以计算机视觉、模式识别和人工智能相关技术为基础的矿井危险区域目标行为检测与跟踪,是智能视觉监控在煤矿的重要应用,将矿井视频监控从事后取证改为基于事前预防和实时事件驱动的监控方式,使煤矿综合自动化系统基于视频的报警联动成为可能,而目标跟踪作为关键技术直接决定了系统性能。运动目标跟踪不仅可以提供目标的运动轨迹和实现目标准确定位,为下一步的目标行为分析与理解提供可靠的数据来源,而且也可以为运动目标检测提供帮助,从而形成一个良性的循环。
粒子滤波采用蒙特卡罗仿真来近似贝叶斯滤波,在有关非线性非高斯系统的数据处理和分析领域为估计目标状态的后验概率分布提供了一种方便、高效的解决方法。针对粒子滤波在矿井视频目标跟踪中存在的问题,本文在粒子滤波多观测模型以及融合策略、粒子滤波建议分布、粒子滤波重采样算法以及基于粒子滤波的多目标跟踪等方面开展了研究。
本文的主要工作如下:
针对CONDENSATION算法以状态转移作为建议分布从而导致“权值蜕化”问题,本文提出了以迭代最小斜度单型sigmaUKF建立建议分布的UPF算法。以最小斜度单型UKF产生统计线性误差项,再对IEKF推导产生不依赖于系统非线性映射Jacobian矩阵的迭代式,以此对状态均值和协方差进行迭代修正以近似0残差使状态收敛到MAP估计,平滑了状态一步预测误差,从而提高估计精度。结果表明:该算法扩大了预测样本与观测似然峰值区的重叠区域,提高了非线性系统的状态估计精度。
针对矿井跟踪视场中由于单一线索对目标特征描述缺乏可分性以及多线索融合策略对场景变化缺乏自适应性导致人员跟踪失效的问题,本文提出了基于自适应多测量融合粒子滤波的矿井人员跟踪算法。将粒子邻域光流统计信息表征的运动性作为线索建立运动光流直方图模型,并与颜色相融合建立多观测模型;采用单观测估计状态粒子区域与融合估计粒子区域的质心距离作为单观测模型贡献率度量因子,定义了观测权值自适应策略,实现了粒子观测模型与跟踪目标状态特征的同步变化,通过采样补偿函数对粒子落入低观测似然时进行有效的采样补偿增强跟踪的鲁棒性。结果表明:本算法能够有效的解决矿井跟踪视场下(背景复杂)由于观测模型失效而导致的跟踪发散问题。
针对粒子滤波中如何设计重采样策略解决“权值蜕化”的同时又避免“样本贫化”的问题,提出了基于分层转移的MCMC重采样算法。该算法打破了传统重采样以大权样本子代替换蜕化样本的思路,当样本容量检测出现“蜕化”时,将样本集据权值蜕化程度分层,提出变异繁殖算法,将其与PSO融合产生MCMC转移核施以分层子集;再通过Metroplis-Hastings算法做接收-拒绝采样,由此构建的Markov Chain收敛到与目标真实后验等价的平稳分布。结果表明:本算法以更快的收敛速度更小的估计误差贴近目标真实后验,提高了估计精度。
针对矿井危险区域目标跟踪在多摄像机下无法协同的问题,本文提出一种多摄像机下基于目标区域的配准算法。采用Lucas-Kanade光流法和基于块的背景运动补偿分别实现静态背景和复杂背景下的目标区域分割;用DOG对目标区域做尺度空间极值检测获得特征点对,并用PCA-SIFT描述子作特征区域描述,在定义的目标区域匹配准则下,通过目标区域匹配度量比较实现匹配并通过基本矩阵约束消除误配区域。结果表明:该算法能够快速有效的实现矿井危险区域(低照度、目标类型复杂)多摄像机下的运动目标匹配。
针对多目标聚集时经典JPDA-PF导致的跟踪发散问题,提出多目标聚集情况下快速联合概率数据关联算法,并与粒子滤波结合,实现矿井跟踪视场下多目标鲁棒跟踪。对跟踪门逻辑输出进行回波Bhattacharya系数比较,确定航迹的起始/消除;将波门内回波区域Bhattacharya均值作为量测与目标的关联度量,构建回波区域Bhattacharya均值矩阵,对确认矩阵元素取值进行修正,消除小概率关联事件对确认矩阵拆分复杂度的影响。结果表明:本算法实现低计算开销的同时保持了较高估计精度,当目标密集分布时关联准确率明显优于经典JPDA。
Behavior detection and tracking of targets in coal mine dangerous regions,which is dependent on Computer Vision,Pattern Recognition and Artificial Intelligence, etc, has become significant application of Intelligent Visual Surveillance(IVS) in coal mines. In mine IVS systems, targets tracking is a key technology and directly determines its performance. Tracking not only provides accurate positions of targets and data sources for further analysis, but also optimizes the targets detection.
Particle filter, using a great deal of stochastic weighted samples to approximating Bayesian filter,has the great advantage of not being subject to the assumption of linearity or Gaussianity of the model in targets states estimation. To improve the robustness of particle filter based tracking algorithms in coal mines, the research is focused on the four major aspects of particle filter: observation models and multi-cues integration strategy, proposal distribution, re-sampling algorithms and Multi-targets Tracking based on particle filter.
The main research work includes:
A novel unscented particle filter algorithm (ISUKF-PF) has been proposed, using iterated minimal skew simplex UKF (ISUKF) as the proposal distribution. In this paper, statistical liner error propagations were obtained by ISUKF;And we have derived the IEKF iterated equations by replacing the system model Jacobian matrix with statistical liner error propagation terms; Then the states mean and covariance have been iterated and updated by the IEKF iterated equations to be convergent to the state MAP estimation for near zero-residual. The results show that the ISUKF achieved the more overlap regions of prediction samples and peak zones of observation likelihood and increased the accuracy of state estimating in nonlinear system.
A novel unscented particle filter(UPF) algorithm has been proposed for object-tracking in coal mines, using ISUKF as proposal distribution and an adaptive multi-cues fusion modal as the observation modal. ISUKF generate prediction samples adjusted to high likelihood area in state-space. Then, Optical flow histogram was proposed and observation model based on multi-cues fusion was implemented by integrating optical flow with color. Adaptive strategy of observation modal weights was implemented by adjusting the contribution rate of single-cue observation modal. Consequently, reliability of an observation modal adjusted to changes of object characteristics accordingly. Finally, a function of sample compensation was proposed to handle particle diffusion due to failure of observation modal. The results show that the tracking algorithm is an effective solution to tracking failure due to invalidation of observation modal in coal mines(complex background).
A new method, named layered transacting MCMC-Resampling algorithm, was proposed. When the effective sample size is below a fixed threshold, particles are dived into two sample subsets according to their individual weights. Mutation operator and PSO, which considered as transition kernels of MCMC, applied to sample subsets respectively. Then an acceptance-rejection rule of Metropolis-Hastings algorithm is applied to generate the Markov Chain with the stationary distribution which is equivalent to target posterior density. The results show that the proposed method is superior to the other resampling algorithms both in accuracy and convergence speed.
A new object matching algorithm based on object-regions in multiple cameras was proposed. The scale invariant features of object-region were extracted, which used difference-of-gaussian (DOG) algorithm. The region was represented by PCA-SIFT descriptor. Through comparing the matching values of object-regions, objects matching was completed in object-regions matching algorithm; And then, wrong regions were deleted by the fundamental matrix. The results show that the matching algorithm can deal with object matching in coal mine(low illumination level、complex target type) based multiple cameras.
An improved JPDA algorithm,approximating confirmation matrix of JPDA by measurements Bhattacharya coefficients,was proposed . Track birth and death was obtained by comparing Bhattacharya coefficients of measurements in a tracking gate. Then Bhattacharya means matrix of measurements area was created and elements of confirmation matrix were optimized to eliminating association events with low association probability. The results show that the proposed method reduced the computational complexity as well as increased precision of estimation.
粒子滤波采用蒙特卡罗仿真来近似贝叶斯滤波,在有关非线性非高斯系统的数据处理和分析领域为估计目标状态的后验概率分布提供了一种方便、高效的解决方法。针对粒子滤波在矿井视频目标跟踪中存在的问题,本文在粒子滤波多观测模型以及融合策略、粒子滤波建议分布、粒子滤波重采样算法以及基于粒子滤波的多目标跟踪等方面开展了研究。
本文的主要工作如下:
针对CONDENSATION算法以状态转移作为建议分布从而导致“权值蜕化”问题,本文提出了以迭代最小斜度单型sigmaUKF建立建议分布的UPF算法。以最小斜度单型UKF产生统计线性误差项,再对IEKF推导产生不依赖于系统非线性映射Jacobian矩阵的迭代式,以此对状态均值和协方差进行迭代修正以近似0残差使状态收敛到MAP估计,平滑了状态一步预测误差,从而提高估计精度。结果表明:该算法扩大了预测样本与观测似然峰值区的重叠区域,提高了非线性系统的状态估计精度。
针对矿井跟踪视场中由于单一线索对目标特征描述缺乏可分性以及多线索融合策略对场景变化缺乏自适应性导致人员跟踪失效的问题,本文提出了基于自适应多测量融合粒子滤波的矿井人员跟踪算法。将粒子邻域光流统计信息表征的运动性作为线索建立运动光流直方图模型,并与颜色相融合建立多观测模型;采用单观测估计状态粒子区域与融合估计粒子区域的质心距离作为单观测模型贡献率度量因子,定义了观测权值自适应策略,实现了粒子观测模型与跟踪目标状态特征的同步变化,通过采样补偿函数对粒子落入低观测似然时进行有效的采样补偿增强跟踪的鲁棒性。结果表明:本算法能够有效的解决矿井跟踪视场下(背景复杂)由于观测模型失效而导致的跟踪发散问题。
针对粒子滤波中如何设计重采样策略解决“权值蜕化”的同时又避免“样本贫化”的问题,提出了基于分层转移的MCMC重采样算法。该算法打破了传统重采样以大权样本子代替换蜕化样本的思路,当样本容量检测出现“蜕化”时,将样本集据权值蜕化程度分层,提出变异繁殖算法,将其与PSO融合产生MCMC转移核施以分层子集;再通过Metroplis-Hastings算法做接收-拒绝采样,由此构建的Markov Chain收敛到与目标真实后验等价的平稳分布。结果表明:本算法以更快的收敛速度更小的估计误差贴近目标真实后验,提高了估计精度。
针对矿井危险区域目标跟踪在多摄像机下无法协同的问题,本文提出一种多摄像机下基于目标区域的配准算法。采用Lucas-Kanade光流法和基于块的背景运动补偿分别实现静态背景和复杂背景下的目标区域分割;用DOG对目标区域做尺度空间极值检测获得特征点对,并用PCA-SIFT描述子作特征区域描述,在定义的目标区域匹配准则下,通过目标区域匹配度量比较实现匹配并通过基本矩阵约束消除误配区域。结果表明:该算法能够快速有效的实现矿井危险区域(低照度、目标类型复杂)多摄像机下的运动目标匹配。
针对多目标聚集时经典JPDA-PF导致的跟踪发散问题,提出多目标聚集情况下快速联合概率数据关联算法,并与粒子滤波结合,实现矿井跟踪视场下多目标鲁棒跟踪。对跟踪门逻辑输出进行回波Bhattacharya系数比较,确定航迹的起始/消除;将波门内回波区域Bhattacharya均值作为量测与目标的关联度量,构建回波区域Bhattacharya均值矩阵,对确认矩阵元素取值进行修正,消除小概率关联事件对确认矩阵拆分复杂度的影响。结果表明:本算法实现低计算开销的同时保持了较高估计精度,当目标密集分布时关联准确率明显优于经典JPDA。
Behavior detection and tracking of targets in coal mine dangerous regions,which is dependent on Computer Vision,Pattern Recognition and Artificial Intelligence, etc, has become significant application of Intelligent Visual Surveillance(IVS) in coal mines. In mine IVS systems, targets tracking is a key technology and directly determines its performance. Tracking not only provides accurate positions of targets and data sources for further analysis, but also optimizes the targets detection.
Particle filter, using a great deal of stochastic weighted samples to approximating Bayesian filter,has the great advantage of not being subject to the assumption of linearity or Gaussianity of the model in targets states estimation. To improve the robustness of particle filter based tracking algorithms in coal mines, the research is focused on the four major aspects of particle filter: observation models and multi-cues integration strategy, proposal distribution, re-sampling algorithms and Multi-targets Tracking based on particle filter.
The main research work includes:
A novel unscented particle filter algorithm (ISUKF-PF) has been proposed, using iterated minimal skew simplex UKF (ISUKF) as the proposal distribution. In this paper, statistical liner error propagations were obtained by ISUKF;And we have derived the IEKF iterated equations by replacing the system model Jacobian matrix with statistical liner error propagation terms; Then the states mean and covariance have been iterated and updated by the IEKF iterated equations to be convergent to the state MAP estimation for near zero-residual. The results show that the ISUKF achieved the more overlap regions of prediction samples and peak zones of observation likelihood and increased the accuracy of state estimating in nonlinear system.
A novel unscented particle filter(UPF) algorithm has been proposed for object-tracking in coal mines, using ISUKF as proposal distribution and an adaptive multi-cues fusion modal as the observation modal. ISUKF generate prediction samples adjusted to high likelihood area in state-space. Then, Optical flow histogram was proposed and observation model based on multi-cues fusion was implemented by integrating optical flow with color. Adaptive strategy of observation modal weights was implemented by adjusting the contribution rate of single-cue observation modal. Consequently, reliability of an observation modal adjusted to changes of object characteristics accordingly. Finally, a function of sample compensation was proposed to handle particle diffusion due to failure of observation modal. The results show that the tracking algorithm is an effective solution to tracking failure due to invalidation of observation modal in coal mines(complex background).
A new method, named layered transacting MCMC-Resampling algorithm, was proposed. When the effective sample size is below a fixed threshold, particles are dived into two sample subsets according to their individual weights. Mutation operator and PSO, which considered as transition kernels of MCMC, applied to sample subsets respectively. Then an acceptance-rejection rule of Metropolis-Hastings algorithm is applied to generate the Markov Chain with the stationary distribution which is equivalent to target posterior density. The results show that the proposed method is superior to the other resampling algorithms both in accuracy and convergence speed.
A new object matching algorithm based on object-regions in multiple cameras was proposed. The scale invariant features of object-region were extracted, which used difference-of-gaussian (DOG) algorithm. The region was represented by PCA-SIFT descriptor. Through comparing the matching values of object-regions, objects matching was completed in object-regions matching algorithm; And then, wrong regions were deleted by the fundamental matrix. The results show that the matching algorithm can deal with object matching in coal mine(low illumination level、complex target type) based multiple cameras.
An improved JPDA algorithm,approximating confirmation matrix of JPDA by measurements Bhattacharya coefficients,was proposed . Track birth and death was obtained by comparing Bhattacharya coefficients of measurements in a tracking gate. Then Bhattacharya means matrix of measurements area was created and elements of confirmation matrix were optimized to eliminating association events with low association probability. The results show that the proposed method reduced the computational complexity as well as increased precision of estimation.
引文
[1] Liu J.S.and Chen R.,Sequential Monte Carlo Methods for Dynamical Systems[J],Journal of the American Statistical Association,1998,93(5):1032-1044.
[2] Arulampalam S.,Maskell S.,Gordon N.,and Clapp T.,A Tutorial on Particle Filters for Online Non-linear/Non-Gaussian Bayesian Tracking[J], IEEE Transactions on Signal Processing,2002, 50 (2):174-188.
[3] Kitagawa G.,Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models[J],Journal of Computational and Graphical Statistics,1996,5(1):1-25.
[4] Van der Merwe R.,Doucet A.,Freitas N.,and Wan E.A.,The Unscented Particle Filter.[C]In Advances in Neural Information Processing Systems(NIPS13),MIT Press,2000.
[5] Doucet A.,Godsill S.J.,and Andrieu C.,On Sequential Monte Carlo Sampling Methods for Bayesian Filtering[J],Statistics and Computing,2000,10(3):197-208.
[6] Doucet A.,Freitas N.,and Gordon N., Sequential Monte Carlo Methods in Practice.[M] Springer-Verlag Press,2001.
[7] Gordon N.J.,Salmond D.J.,and Smith A.F.M.,Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation.[C] In IEE Proceedings on Radar and Signal Processing,1993,140(2):107-113.
[8] Hue C.,Le Cadre J-P.,and Perez P.,Sequential Monte Carlo Methods for Multiple Target Tracking and Data fusion.[J] IEEE Transaction on Signal Processing,2002,50(2):309-325.
[9] Isard M.and Blake A.,Condensation-conditional Density Propagation for Visual Tracking.[J] International Journal on Computer Vision,1998,29(1):5-28.
[10] Koller-Meier E.B.and Ade F.,Tracking Multiple Objects Using the Condensation Algorithm.[J] Journal of Robotics and Autonomous Systems,2001,34:93-105.
[11] Kanazawa K.,Koller D.,and Russell S.J.,Stochastic Simulation Algorithms for Dynamic Probabilistic Networks.[C] In Proceedings of the 11th Annual Conference on Uncertainty in AI,1995,346-351.
[12]华钢,周磊,任凯.地下煤矿人员定位跟踪系统发展综述[J].矿山机械,2008,36(6)36-39.
[13]王仁龙.国内煤矿井下人员定位系统现状探讨[J].水力采煤与管道运输,2008,(3):69-72.
[14]朱华,葛世荣,余小燕,等.矿井人员红外探测计数技术研究[J].中国矿业大学学报,2003,32(2):165-168.
[15]钱建生,蔡利梅.煤矿井下斜巷轨道运输综合监控系统[J].煤矿安全,2009,(S1):133-135.
[16]张波.基于粒子滤波的图像跟踪算法的研究[D].上海:上海交通大学电子信息与电气工程学院, 2007.
[17]候志强,韩崇昭.视觉跟踪技术综述.[J]自动化学报,2006, 32(4): 603-618.
[18] M.J.Black and A.D.Jepson.EigenTracking:Robust matching and tracking of articulated objects using a view-based representation.[J]International Journal of Computer Vision,1998,26(1):63-84.
[19] I.Cohen and G.Medioni.Detecting and tracking moving objects for video surveillance. [C]In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Fort Collins,CO,USA,1999,2,319-325.
[20] I.Haritaoglu, D.Harwood and L.S.Davis.W4:Real-time surveillance of people and their activities.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22 (8):809-830.
[21] P.KaewTrakulPong and R.Bowden.A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes.[J] Image and Vision Computing,2003, 21(10):913-929.
[22] T.Zhao and R.Nevatia.Tracking multiple humans in complex situations.[J]IEEE Transactions On Pattern Analysis and Machine Intelligence,2004,26(9):1208-1221.
[23] J.Wang and M.F.Cohen.Very low frame-rate video streaming for face-to-face teleconference.[C] In:Proceedings of Data Compression Conference Snowbird,UT,United States,2005,309-318.
[24] J.MacCormick and A.Blake.A probabilistic exclusion principle for tracking multiple objects.[J]International Journal of Computer Vision,2000,39(1):57-71.
[25] A.Pentland.Looking at people:Sensing for ubiquitous and wearable computing.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(1):107-119.
[26] H.Nait-Charif and S.J.McKenna.Head tracking and action recognition in a smart meeting room.[C] In:Proceedings of 4th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance,Graz,Austria,2003,24-31.
[27] Y.Wu and T.S.Huang.Robust visual tracking by integrating multiple cues based on co-inference learning.[J]International Journal of Computer Vision,2004,58(1):55-71.
[28] C.F.Shan,Y.C.Wei,T.N.Tan,et al.Real time hand tracking by combining particle filtering and mean shift.[C]In:Proceedings of 6th International Conference on Automatic Face and Gesture Recognition,2004,669-674.
[29] N.P.Papanikolopoulos,P.K.Khosla and T.Kanade.Visual tracking of a moving target by acamera mounted on a robot:a combination of control and vision.[J]IEEE Transactions on Robotics and Automation,1993,9(1):14-35.
[30] B.Jung and G.S.Sukhatme.A generalized region-based approach for multi-target tracking in outdoor environments.[C]In:Proceedings of IEEE International Conference on Robotics and Automation, New Orleans,LA,United States,2004,2004,2189-2195.
[31] B.Jung and G.S.Sukhatme.Detecting moving objects using a single camera on a mobile robot In an outdoor environment.[C]In:Proceedings of International Conference on Intelligent Autonomous Systems,2004,980–987.
[32] L.Mun Wai,I.Cohen and J.Soon Ki.Particle filter with analytical inference for human body tracking.[C]In:Proceedings of IEEE Workshop on Motion and VideoComputing,2002,159-165.
[33] H.Z.Ning,T.N.Tan,L.Wang,et al.People tracking based on motion model and motion constraints with automatic initialization.[J]Pattern Recognition,2004,37(7):1423-1440.
[34] T.Xiang and S.G.Gong.Beyond tracking:Modelling activity and understanding behaviour.[J International Journal of Computer Vision,2006,67(1):21-51.
[35] C.Shen. Robust visual tracking in image sequences.[D] The University of Adelaide,2005.
[36] D.Comaniciu,V.Ramesh and P.Meer.Kernel-based object tracking.[J]IEEE Transaction on Pattern Analysis and Machine Intelligence,2003,25(5):564-577.
[37] P.Perez,C.Hue,J.Vermaak,et al.Color-based probabilistic tracking.[C]In:Proceedings of 7th European Conference on Computer Vision,2002,661-675.
[38] K.Nummiaro,E.Koller-Meier and L.Van Gool.An adaptive color-based particle filter.[J]Image And Vision Computing,2003,21(1):99-110.
[39] S.T.Birchfield and S.Rangarajan.Spatiograms versus histograms for region-based tracking.[C] In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,United States,2005,2,1158-1163.
[40] N.Peterfreund.Robust tracking of position and velocity with Kalman snakes.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(6):564-569.
[41] M.Isard and A.Blake.CONDENSATION-Conditional density propagation for visual tracking.[J] International Journal of Computer Vision,1998,29(1):5-28.
[42] P.H.Li,T.W.Zhang and A.E.C.Pece.Visual contour tracking based on particle filters.[J]Image and Vision Computing,2003,21(1):111-123.
[43] J.Deutscher and I.Reid.Articulated body motion capture by stochastic search.[J]International Journal of Computer Vision,2005,61(2):185-205.
[44] Y.Chen,Y.Rui and T.S.Huang.JPDAF based HMM for real-time contour tracking.[C]In: Proceedings of IEEE Computer Society Conference on Computer Vision and PatternRecognition,Kauai,Hawaii,2001,1,543-550.
[45] C.Shen,A.van den Hengel and A.Dick. Probabilistic multiple cue integration for particle filter based tracking.[C] In: International Conference on Digital Image Computing: Techniques and Applications,Sydney,2003,1,399–408.
[46] A.Li, Z.Jing and S.Hu. Particle filter based visual tracking with multi-cue adaptive fusion.[J] Chinese Optics Letters,2005,3(6):326-329.
[47] P.Perez,J.Vermaak and A.Blake. Data fusion for visual tracking with particles. [J]Proceedings Of the IEEE,2004,92(3):495-513.
[48] E.Maggio,F.Smeraldi and A.Cavallaro. Combining colour and orientation for adaptive Particle filter-based tracking.[C] In:Proceedings of British Machine Vision Conference Oxford,UK 2005.
[49] B.Han,C.Yang,R.Duraiswami,et al. Bayesian filtering and integral image for visual Tracking [C] In: Proceedings of 6th International Worshop on Image Analysis for Multimedia Interactive Services Montreux,Switzerland,2005.
[50] C.Yang,R.Duraiswami and L.Davis. Fast multiple object tracking via a hierarchical particle filter.[C]In:Proceedings of IEEE International Conference on Computer Vision,Beijing,China,2005,I,212-219.
[51] D.DeCarlo and D.Metaxas.Optical flow constraints on deformable models with applications To face tracking.[J]International Journal of Computer Vision,2000,38(2):99-127.
[52] A.D.Jepson,D.J.Fleet and T.F.El-Maraghi.Robust online appearance models for visual tracking.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1296-1311.
[53] P.Viola and M.J.Jones.Robust real-time face detection.[J]International Journal of Computer Vision,2004,57(2):137-154.
[54] T.Ojala,M.Pietikainen and T.Maenpaa.Multiresolution gray-scale and rotation invariant Texture classification with local binary patterns.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[55] C.Wen-Yan,C.Chu-Song and H.Yi-Ping.Discriminative descriptor-based observation model For visual tracking.[C]In:Proceedings of 18th International Conference on Pattern Recognition,Hong Kong,2006,3,83-86.
[56] C.Yang, R.Duraiswami and L.Davis. Efficient mean-shift tracking via a new similarity measure.[C] In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, United States,2005,I,176-183.
[57] H.Wang,D.Suter and K.Schindler. Effective appearance model and similarity measure forparticle filtering and visual tracking.[C]In:Proceedings of 9th European Conference on Computer Vision,Graz,Austria,2006,(3),606-618.
[58] J.Lim,D.Ross,R.S.Lin,et al..Incremental learning for visual tracking.[C]In:L.Saul,et al.(Eds.),Advances in Neural Information Processing Systems,MIT Press,2005.
[59] J.Ho,K.-C.Lee,M.-H.Yang,et al.Visual tracking using learned linear subspaces.[C]In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Washington,DC,United States,2004,1,782-789.
[60] C.Shen,A.Van Den Hengel and M.J.Brooks.Visual tracking via efficient kernel discriminant subspace learning.[C]In:Proceedings of International Conference on Image Processing,Genova,Italy,2005,2,590-593.
[61] K.-C.Lee and D.Kriegman.Online learning of probabilistic appearance manifolds for video-based recognition and tracking.[C]In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,United States,2005,I,852-859.
[62] C.Sminchisescu and A.Jepon.Generative modeling for continuous non-linearly embedded Visual inference.[C] In:Proceeding of International Conference on Machine Learning,Banff, Canada,2004.
[63] R.T.Collins,Y.X.Liu and M.Leordeanu.Online selection of discriminative tracking features.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1631-1643.
[64] J.Wang,X.Chen and W.Gao. Online selecting discriminative tracking features using particle filter.[C]In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,United States,2005,2,1037-1042.
[65] S.Avidan. Support vector tracking.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(8):1064-1072.
[66] S.Avidan. Ensemble tracking.[C]In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,United States,2005,2,494-501.
[67] P.Viola and M.Jones.Rapid object detection using a boosted cascade of simple features.[C]In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Kauai,HI,2001,1,511-518.
[68] H.Grabner,M.Grabner and H.Bischof.Real time tracking via on-line boosting.[C]In:British Machine Vision proceedings,2006,1,47-57.
[69] R.Van der Merwe,A.Doucet,N.de Freitas,et al. The unscented particle filter.[R] Cambridge niversity Engineering Department,August,2000.
[70] E.Wan and R.van der Merwe. The unscented Kalman Filter[R]. Cambridge University Engineering Department,August,2000.
[71]彭宁嵩,杨杰,刘志,等.Mean-Shift跟踪算法中核函数窗宽的自动选取.[J]软件学报,2005,16(9):1542-1551.
[72] J.Tu,H.Tao and T.Huang. Online updating appearance generative mixture model for Meanshift tracking.[C]In:Proceedings of Asian Conference on Computer Vision,Hyderabad,India,2006,1,694-703.
[73] R.van der Merwe.Sigma-point Kalman Filters for probabilistic inference in dynamic state-space models.[D]Oregon Health and Science University,2004.
[74]程水英,张剑云.粒子滤波综述[J].宇航学报,2008,29(4):1099-1110.
[75] Haykin S Ed. . Kalman Filtering and Neural Networks [M] . NewYork : John Wiley &Sons , 2001 : 5 - 10 , 16 - 20 , 253 - 267.
[76] Bar-Shalom Y, Li X R , Kirubarajan T. Estimation with Applications to Tracking and Navigation : Theory , Algorithms , and Software[M] .New York : John Wiley & Sons , 2001 : 373 - 374 , 200 - 217 , 381- 395.
[77] Arulampalam M S , Maskell S , Gordon N , et al . A tutorial on particle filters for online Nonlinear/non-Gaussian Bayesian tracking [ J ] .IEEE Trans. Signal Processing , 2002 , 50 (2) : 174 - 188.
[78] Chen Zhe. Bayesian Filtering : From Kalman Filters to Particle Filters , and Beyond[ R] .Hamilton : McMaster University , 2003.
[79] Uhlmann J K. Algorithm for multiple target tracking[J]American Science, 1992. 80(2):128-141
[80] Sorenson H W, Kalman Filtering: Theory and Application[M].New York: IEEE Press, 1985.
[81] Dan Simon. Optimal State Estimation : Kalman , H∞, and Nonlinear Approaches[M] . New Jersey : John Wiley &Sons , 2006 , 130 : 121- 330 , 400 - 409 , 158 - 173.
[82] Briers M, Maskell S R , Wright R. A rao-blackwellised unscented Kalman filter[C] . In : Proc.the Sixth Int . Conf . Information Fusion , Cairns , Australia , 2003 : 55 - 61.
[83] R van der Merwe , Wan E. The square-root unscented kalman filter for state and Parameter estimation[C] . In : Proceedings of the International Conference on Acoustics , Speech , and Signal Processing( ICASSP) , Salt Lake City , 2001 : 3461 - 3464.
[84] R. van der Merwe , Wan E. Efficient derivative-free Kalman filters for online learning[C]//2001 Proc. European Symposium on Artificial Neural Networks (ESANN) , Bruges , 2001 : 205 - 210.
[85]柴霖,袁建平,罗建军,等.非线性估计理论的最新进展[J].宇航学报, 2005 , 26 (3) : 380- 384.
[86]苏卡尼,吕明.插值滤波器在单站外辐射源无源定位中的应用[J] .舰船电子工程, 2006 , 26 (4) : 146 - 148 , 162.
[87] Ito K, Xiong K. Gaussian filters for nonlinear filtering problems[J ] .IEEE Trans. Automat . Contr. , 2000 , 45 (5) : 910 - 927.
[88]袁泽剑,郑南宁,贾新春.高斯-厄米特粒子滤波器[J ] .电子学报, 2003 , 31 (7) : 970 - 973.
[89]程水英,邹继伟,汤鹏.免微分非线性Bayesian滤波方法评述[J].宇航学报2009,30(3):843-859.
[90] Bergman N. Recursive Bayesian estimation-navigation and tracking applications[D]. Linkêping : Linkêping University , 1999.
[91] Carpenter J , Clifford P , Fearnhead P. Improved particle filter for nonlinear problems[J ] .IEE Proc.-Radar , Sonar Navig. , 1999 : 146(1) : 1 - 7.
[92] Marshall A. The use of multi-stage sampling schemes in Monte Carlocomputations [C]\\Meyer M. Symposium on Monte Carlo Methods.New York : 1956 : 123 - 140.
[93] Doucet A , Gordon NJ , Krishnamurthy V. Particle filters for state estimation of jump Markov linear systems[J ] . IEEE Trans. Signal Processing , 2001 , 49 (49) : 613 - 624.
[94] Kong A , Liu J S , Wong W H. Sequential imputations and Bayesian missing data problems [ J ] . J . American Statistical Association , 1994 , 89 : 278 - 288.
[95] Herman S M. A particle filtering approach to joint passive radar tracking and target classification[D] . Illinois : University of Illinois at Urbana-Champaign , 2002.
[96] Isard M, Blake A. CONDENSATION-conditional density propagation for visual tracking[J ] . Int . J . Computer Vision , 1998 , 29 (1) : 5 -28.
[97] MacCormick J , Isard M. Partitioned sampling , articulated objects ,and interface-quality hand tracking[ R] . Oxford : University of Oxford , 2000.
[98]姚剑敏.粒子滤波跟踪方法研究[D]长春:中国科学院长春光学精密机械与物理研究所.
[99] Spall J C. Estimation via markov chain monte carlo[J ] . IEEE Control Systems Magazine , 2003 , 23 (2) : 34 - 45.
[100] Chen Z, Haykin S. On different facets of regularization theory[J ] .Neural Comput .2002 14 (12) : 2791 - 2846.
[101]程水英,张剑云.裂变自举粒子滤波[J ] .电子学报, 2008 ,36 (3) : 500 - 504.
[102] Crisan D , Doucet A. A survey of convergence results on particle filtering methods forpractitioners[J ] . IEEE Trans. Signal Processing ,2002 , 50 (3) : 736 - 746.
[103] Freitas J F G, Niranjan M , Gee A H ,et al. Sequential Monte Carlo Methods to TrainNeuralNetwork Models [J] .Neural Computation , 2000 , 12 (4) : 955-993.
[104]李良群,姬红兵,罗军辉.迭代扩展卡尔曼粒子滤波器[J].西安电子科技大学学报,2007,34(2):233-238.
[105] M.Pitt and N.Shephard. Filtering via simulation:auxiliary particle filters.[J]Journal of the American statistical association,1999,94(446):590-599.
[106] H.Nait-Charif and S.J.McKenna. Tracking poorly modelled motion using particle filters with iterated likelihood weighting.[C]In:Proceedings of Asian Conference on Computer Vision Jeju Island,Korea,2004.
[107] C.H.Shen,H.Anton van den,A.Dick,et al.Enhanced importance sampling:unscented auxiliary particle filtering for visual tracking.[C]In:Proceedings of Australian Conference on Artificial Intelligence,Cairns,Australia,2004,180-191.
[108]杨峰,潘泉,等.多源信息空间配准中的UT变换采样策略研究[J].系统仿真学报,2006,18(3):713-717.
[109]程水英,毛云祥.迭代无味卡尔曼滤波器[J].数据采集与处理,2009,24(S):43-47.
[110] Julier S J , U h lmann J K, Durrant-Whyte H F. A new approach for the nonlinear transformation of means and covariances in filters and estimators[J]. IEEE Transation Automatic Control, 2000, 45(3): 477-482.
[111] Julier, S.J., The scaled unscented transformation [C]//2002 American Control Conference.Anchorage Alaska: IEEE, 2002, 6: 4555– 4559.
[112] Julier S J, Uhlmann J K. Unscented Filtering and Nonlinear Estimation [J]. Proceedings of the IEEE (S0018-9219), 2004, 92(3):401– 422.
[113] Julier S J, Uhlmann J K. A consistent, Debiased method for converting between polar and Cartesian coordinate systems [C]// Proc.AeroSense: Acquisition, Tracking and Pointing XI. Orlando, Florida:SPIE, 1997, 3086: 110–121.
[114] Julier, S.J., Uhlmann, J.K, A new extension of the Kalman filter to nonlinear systems [C]// Proc. AeroSense: 11th Int. Symp.Aerospace/Defense Sensing, Simulation and Controls. Orlando,Florida: SPIE, 1997, 3086: 182-193.
[115] Julier S J. The spherical simplex unscented transformation [C]// 2003 American Control Conference. Denver, Colorado: IEEE, 2003, 3:2430-2434.
[116] Julier S J, Uhlmann J K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations [C]// 2002 American Control Conference. Anchorage Alaska : IEEE, 2002, 2: .887– 892.
[117] PEREZ P, VERMAAK J, BLAKE A. Data fusion for visual tracking with particles [J].Proceedings of the IEEE,2004,92(3):495-513.
[118] WU PL, KONG LF, ZHAO FD, et al. Particle filter tracking based on color and sift features[C] // Audio, Language and Image Processing, 2008. Shanghai: [s. n.], 2008:932-937.
[119] F.Moreno-Noguer. Multiple cue integration for robust tracking in dynamic environments:application to video relighting.Ph.D thesis,Institute of Robotics and Industrial Informatics Technical University of Catalonia,2005.
[120] TAO X, CHRISTIAN D. Monte carlo visual tracking using color histograms and a spatially weighted oriented hausdorff measure[C]//In Proceedings of the Conference on Analysis of Images and Patterns. Groningen: Springer Berlin Heidelberg, 2003:190-197.
[121] KWOLEK B. Stereovision-based head tracking using color and ellipse fitting in a particle filter[C]// Proceedings of the 8th European Conference on Computer Vision. Prague: Springer Berlin Heidelberg, 2004:192-204.
[122] SPENGLER M, SCHIELE B. Towards robust multi-cue integration for visual tracking [J].Machine Vision and Applications,2003. 14 (1): 50–58.
[123] LOWE DG. Distinctive image features from scale-invariant keypoints [J].International Journal of Computer Vision , 2004 , 60 (2) : 91-110.
[124]管志强,陈钱,顾国华,等.基于光流直方图的云背景下低帧频小目标探测方法[J].光学学报,2009,28(8):1496-1501.
[125] LUCAS B D, KANADE T. An iterative image registration technique with an application to stereo vision[C]//Proc 7th International Joint Conf on Artificial Intelligence. Vancouver:[s. n.], 1981 :674-679.
[126] B. Ristic, S. Arulampalam, N. Gordon. Beyond the kalman filter:particle filter for tracking application[M]. Boston: Artech House,2004.
[127] N.M.Kwok, F.Gu, WZ.Zhou. Evolutionary particle filter: re-sampling from the genetic algorithm perspective[C]. Intelligent Robots and Systems 2005,Edmonton,Alberta,Canada, Aug2-6.2005, 2935-2940.
[128] Y.Zhai, M.Yeary. Implementing particle filters with metropolis-hastings algorithms[C]. Region 5 Conference: Annual Technical and Leadership Workshop, IEEE Press,April.2004, 149-152.
[129]齐欢,王小平.系统建模与仿真[M].北京:清华大学出版社, 2004.
[130] J D Hol. Resampling in Particle Filters [R]// Sweden: Link?ping University, 2004.
[131]吴宝成.粒子滤波重采样算法研究及其应用[D/OL].中国优秀硕士学位论文全文数据库, 2007, http://dlib.cnki.net/kns50.
[132] Miodrag Bolic’, Peter M Djuric’, Sangjin Hong. New Resampling Algorithms for ParticleFilters [J]. ICASSP2003 (S1845-5921), 2003,vol.2: 589-592.
[133] B.A.Berg. Markov chain monte carlo simulations and their statistical analysis[M]. Singapore:World Scientific,2004.
[134] Christian P Robert. George Casella. Monte Carlo statistical methods[M]. New York : Springer,2004.
[135] R. Y. Rubinstein and D. P. Kroese. Simulation and the Monte Carlo Method (second edition)[M]. New York: John Wiley & Sons, 2007.
[136] Christophe Andrieu, Nando de Freitas, et al. An Introduction to MCMC for Machine Learning[J]Machine Learning, 2003,Vol. 50, No. 1. pp: 5-43.
[137] M.Clerc, J.Kennedy, The particle swarm: Explosion, stability, and convergence in a multimentional complex space[J].IEEE Trans.Evol.Comput.2002,6(1): 58-73.
[138] Kennedy,J. Eberhart,R.C. Swarm Intelligence (The Morgan Kaufmann Series in Artificial Intelligence) .Morgan Kaufmann,2001.
[139] Poli, R. An analysis of publications on particle swarm optimisation applications[R]. University of Essex Department of Computer Science.2007.
[140] Trelea, I.C. The Particle Swarm Optimization Algorithm: convergence analysis and parameter selection[J]. Information Processing Letters ,2007, 85( 6):317–325.
[141] KELLY P H, KATKERE A, KURAMURA D Y, et al. An architecture for multiple perspective interactive video: proc of the 3nd ACM International Conference on Multimedia [C]// San Francisco: [ s. n. ] ,1995: 201-212.
[142] ZHOU Qu-ming, AGGARWAL J K. Object tracking in an outdoor environment using fusion of features and cameras[J].Image and Vision Computing, 2006, 24(11): 1244- 1255.
[143] XU M, ORWELL J, LOWEY L, et al. Architecture and algorithms for tracking football players with multiple cameras[J] Image and Signal Processing ,2005,152(2):232-241.
[144] JAVERD O, RASHEED Z, SHAFIQUE K, et al. Tracking across multiple cameras with isjoint views[C]//Proceedings of IEEE International Conference on Computer Vision. Nice: ACM Press, 2003:165- 171.
[145] CALDERARA S, PRATI A, VEZZANI R, et al. Consistent labeling for multicamera object tracking[J].IEEE Trans on PAMI , 2003, 25( 10) :1355- 1360.
[146] Bar-Shalom Y, Fortmann T E. Tracking and data association [M] .New York :Academic , 1988.
[147] Mori S, Chong C, Tse T, et al.. Tracking and classify in multiple targets without a priori identification. IEEE Trans. on Automatic,Control,1986,AC-31(5):401-409.
[148] Thomas E.Fortmann, Yaakov Bar-Shalom. Sonar Tracking of Multiple Targets Using JointProbabilistic Data Association [J].IEEE Journal of Oceanic Engineering (S0364-9059), 1983, 8(3): 173-184.
[149] Kuo Chu Chang, Yaakov Bar-Shalom. Joint Probabilistic DataAssociation for Multitarget Tracking with Possibly Unresolved Measurements and Maneuvers [J]. IEEE Transactions on Automatic Control(S0018-9286), 1983, 29(7): 585-594.
[150] Yaakov Bar-Shalom, X R Li. Multitarget-Multisensor Tracking:Principles and Techniques [M]. Storrs, CT: YBS, 1995.
[151] Songhwai Oh, Stuart Russell, and Shankar Sastry, Markov Chain Monte Carlo Data Association for Multi-Target Tracking[J]. IEEE Trans. On Automatic Control, 2009 ,54(3):481-491.
[152] J.A.Roecker, G.L.Phillis. Suboptimal Joint Probabilistic Data Association [J]. IEEE. Transactions on Aerospace and Electronic Systems(S0018-9251), 1993, 29(2): 510-517.
[153] K. Mikolajczyk, T. Tuytelaars,et al. A comparison of affine region detectors[J]. International Journal of Computer Vision, 2005,65(1):43-72
[154] Mikolajczyk, K. and Schmid, C. Scale & affine invariant interest point detectors[J]. International Journal on Computer Vision, 2004,60(1):63-86.
[155] K. Mikolajczyk and C. Schmid, Scale and Affine invariant interest point detectors[J]. International Journal of Computer Vision,2004, 60(1):63-86.
[156] FISCHLER MA, BOLLES RC. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography [J]. CACM, 1981,24(6):381?395.
[157] HARTLEY R. Multiple view geometry in computer vision[M]. Cambridge: Cambridge University Press,2000.
[158] BARRON J L , FLEET D J , BEAUCHEMIN S S , et al. Performance of optical flow techniques[J]. International Journal of Computer Vision, 1992,12(1):43-77
[159] MIKOLAJCZYK K, SCHMID C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27 (10) : 1615-1630.
[160] YAN KE, RAHUL SUKTHANKAR. PCA-SIFT: A more distinctive representation for local image descriptors [C]// IEEE conference on Computer Vision and Pattern Recognition. Washington: IEEE, 2004: 506-503.
[161]韩崇昭,朱洪艳,段战胜,等.多源信息融合[M] .北京:清华大学出版社,2006 .
[162] LUONG Q T, FAUGERAS O. A stability analysis of the fundamental matrix[C]//Proceedin- gs of the third European conference on Computer vision. New York: Springer Berlin Heidelberg ,1994:577-588.
[2] Arulampalam S.,Maskell S.,Gordon N.,and Clapp T.,A Tutorial on Particle Filters for Online Non-linear/Non-Gaussian Bayesian Tracking[J], IEEE Transactions on Signal Processing,2002, 50 (2):174-188.
[3] Kitagawa G.,Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models[J],Journal of Computational and Graphical Statistics,1996,5(1):1-25.
[4] Van der Merwe R.,Doucet A.,Freitas N.,and Wan E.A.,The Unscented Particle Filter.[C]In Advances in Neural Information Processing Systems(NIPS13),MIT Press,2000.
[5] Doucet A.,Godsill S.J.,and Andrieu C.,On Sequential Monte Carlo Sampling Methods for Bayesian Filtering[J],Statistics and Computing,2000,10(3):197-208.
[6] Doucet A.,Freitas N.,and Gordon N., Sequential Monte Carlo Methods in Practice.[M] Springer-Verlag Press,2001.
[7] Gordon N.J.,Salmond D.J.,and Smith A.F.M.,Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation.[C] In IEE Proceedings on Radar and Signal Processing,1993,140(2):107-113.
[8] Hue C.,Le Cadre J-P.,and Perez P.,Sequential Monte Carlo Methods for Multiple Target Tracking and Data fusion.[J] IEEE Transaction on Signal Processing,2002,50(2):309-325.
[9] Isard M.and Blake A.,Condensation-conditional Density Propagation for Visual Tracking.[J] International Journal on Computer Vision,1998,29(1):5-28.
[10] Koller-Meier E.B.and Ade F.,Tracking Multiple Objects Using the Condensation Algorithm.[J] Journal of Robotics and Autonomous Systems,2001,34:93-105.
[11] Kanazawa K.,Koller D.,and Russell S.J.,Stochastic Simulation Algorithms for Dynamic Probabilistic Networks.[C] In Proceedings of the 11th Annual Conference on Uncertainty in AI,1995,346-351.
[12]华钢,周磊,任凯.地下煤矿人员定位跟踪系统发展综述[J].矿山机械,2008,36(6)36-39.
[13]王仁龙.国内煤矿井下人员定位系统现状探讨[J].水力采煤与管道运输,2008,(3):69-72.
[14]朱华,葛世荣,余小燕,等.矿井人员红外探测计数技术研究[J].中国矿业大学学报,2003,32(2):165-168.
[15]钱建生,蔡利梅.煤矿井下斜巷轨道运输综合监控系统[J].煤矿安全,2009,(S1):133-135.
[16]张波.基于粒子滤波的图像跟踪算法的研究[D].上海:上海交通大学电子信息与电气工程学院, 2007.
[17]候志强,韩崇昭.视觉跟踪技术综述.[J]自动化学报,2006, 32(4): 603-618.
[18] M.J.Black and A.D.Jepson.EigenTracking:Robust matching and tracking of articulated objects using a view-based representation.[J]International Journal of Computer Vision,1998,26(1):63-84.
[19] I.Cohen and G.Medioni.Detecting and tracking moving objects for video surveillance. [C]In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Fort Collins,CO,USA,1999,2,319-325.
[20] I.Haritaoglu, D.Harwood and L.S.Davis.W4:Real-time surveillance of people and their activities.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22 (8):809-830.
[21] P.KaewTrakulPong and R.Bowden.A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes.[J] Image and Vision Computing,2003, 21(10):913-929.
[22] T.Zhao and R.Nevatia.Tracking multiple humans in complex situations.[J]IEEE Transactions On Pattern Analysis and Machine Intelligence,2004,26(9):1208-1221.
[23] J.Wang and M.F.Cohen.Very low frame-rate video streaming for face-to-face teleconference.[C] In:Proceedings of Data Compression Conference Snowbird,UT,United States,2005,309-318.
[24] J.MacCormick and A.Blake.A probabilistic exclusion principle for tracking multiple objects.[J]International Journal of Computer Vision,2000,39(1):57-71.
[25] A.Pentland.Looking at people:Sensing for ubiquitous and wearable computing.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(1):107-119.
[26] H.Nait-Charif and S.J.McKenna.Head tracking and action recognition in a smart meeting room.[C] In:Proceedings of 4th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance,Graz,Austria,2003,24-31.
[27] Y.Wu and T.S.Huang.Robust visual tracking by integrating multiple cues based on co-inference learning.[J]International Journal of Computer Vision,2004,58(1):55-71.
[28] C.F.Shan,Y.C.Wei,T.N.Tan,et al.Real time hand tracking by combining particle filtering and mean shift.[C]In:Proceedings of 6th International Conference on Automatic Face and Gesture Recognition,2004,669-674.
[29] N.P.Papanikolopoulos,P.K.Khosla and T.Kanade.Visual tracking of a moving target by acamera mounted on a robot:a combination of control and vision.[J]IEEE Transactions on Robotics and Automation,1993,9(1):14-35.
[30] B.Jung and G.S.Sukhatme.A generalized region-based approach for multi-target tracking in outdoor environments.[C]In:Proceedings of IEEE International Conference on Robotics and Automation, New Orleans,LA,United States,2004,2004,2189-2195.
[31] B.Jung and G.S.Sukhatme.Detecting moving objects using a single camera on a mobile robot In an outdoor environment.[C]In:Proceedings of International Conference on Intelligent Autonomous Systems,2004,980–987.
[32] L.Mun Wai,I.Cohen and J.Soon Ki.Particle filter with analytical inference for human body tracking.[C]In:Proceedings of IEEE Workshop on Motion and VideoComputing,2002,159-165.
[33] H.Z.Ning,T.N.Tan,L.Wang,et al.People tracking based on motion model and motion constraints with automatic initialization.[J]Pattern Recognition,2004,37(7):1423-1440.
[34] T.Xiang and S.G.Gong.Beyond tracking:Modelling activity and understanding behaviour.[J International Journal of Computer Vision,2006,67(1):21-51.
[35] C.Shen. Robust visual tracking in image sequences.[D] The University of Adelaide,2005.
[36] D.Comaniciu,V.Ramesh and P.Meer.Kernel-based object tracking.[J]IEEE Transaction on Pattern Analysis and Machine Intelligence,2003,25(5):564-577.
[37] P.Perez,C.Hue,J.Vermaak,et al.Color-based probabilistic tracking.[C]In:Proceedings of 7th European Conference on Computer Vision,2002,661-675.
[38] K.Nummiaro,E.Koller-Meier and L.Van Gool.An adaptive color-based particle filter.[J]Image And Vision Computing,2003,21(1):99-110.
[39] S.T.Birchfield and S.Rangarajan.Spatiograms versus histograms for region-based tracking.[C] In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,United States,2005,2,1158-1163.
[40] N.Peterfreund.Robust tracking of position and velocity with Kalman snakes.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(6):564-569.
[41] M.Isard and A.Blake.CONDENSATION-Conditional density propagation for visual tracking.[J] International Journal of Computer Vision,1998,29(1):5-28.
[42] P.H.Li,T.W.Zhang and A.E.C.Pece.Visual contour tracking based on particle filters.[J]Image and Vision Computing,2003,21(1):111-123.
[43] J.Deutscher and I.Reid.Articulated body motion capture by stochastic search.[J]International Journal of Computer Vision,2005,61(2):185-205.
[44] Y.Chen,Y.Rui and T.S.Huang.JPDAF based HMM for real-time contour tracking.[C]In: Proceedings of IEEE Computer Society Conference on Computer Vision and PatternRecognition,Kauai,Hawaii,2001,1,543-550.
[45] C.Shen,A.van den Hengel and A.Dick. Probabilistic multiple cue integration for particle filter based tracking.[C] In: International Conference on Digital Image Computing: Techniques and Applications,Sydney,2003,1,399–408.
[46] A.Li, Z.Jing and S.Hu. Particle filter based visual tracking with multi-cue adaptive fusion.[J] Chinese Optics Letters,2005,3(6):326-329.
[47] P.Perez,J.Vermaak and A.Blake. Data fusion for visual tracking with particles. [J]Proceedings Of the IEEE,2004,92(3):495-513.
[48] E.Maggio,F.Smeraldi and A.Cavallaro. Combining colour and orientation for adaptive Particle filter-based tracking.[C] In:Proceedings of British Machine Vision Conference Oxford,UK 2005.
[49] B.Han,C.Yang,R.Duraiswami,et al. Bayesian filtering and integral image for visual Tracking [C] In: Proceedings of 6th International Worshop on Image Analysis for Multimedia Interactive Services Montreux,Switzerland,2005.
[50] C.Yang,R.Duraiswami and L.Davis. Fast multiple object tracking via a hierarchical particle filter.[C]In:Proceedings of IEEE International Conference on Computer Vision,Beijing,China,2005,I,212-219.
[51] D.DeCarlo and D.Metaxas.Optical flow constraints on deformable models with applications To face tracking.[J]International Journal of Computer Vision,2000,38(2):99-127.
[52] A.D.Jepson,D.J.Fleet and T.F.El-Maraghi.Robust online appearance models for visual tracking.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1296-1311.
[53] P.Viola and M.J.Jones.Robust real-time face detection.[J]International Journal of Computer Vision,2004,57(2):137-154.
[54] T.Ojala,M.Pietikainen and T.Maenpaa.Multiresolution gray-scale and rotation invariant Texture classification with local binary patterns.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[55] C.Wen-Yan,C.Chu-Song and H.Yi-Ping.Discriminative descriptor-based observation model For visual tracking.[C]In:Proceedings of 18th International Conference on Pattern Recognition,Hong Kong,2006,3,83-86.
[56] C.Yang, R.Duraiswami and L.Davis. Efficient mean-shift tracking via a new similarity measure.[C] In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, United States,2005,I,176-183.
[57] H.Wang,D.Suter and K.Schindler. Effective appearance model and similarity measure forparticle filtering and visual tracking.[C]In:Proceedings of 9th European Conference on Computer Vision,Graz,Austria,2006,(3),606-618.
[58] J.Lim,D.Ross,R.S.Lin,et al..Incremental learning for visual tracking.[C]In:L.Saul,et al.(Eds.),Advances in Neural Information Processing Systems,MIT Press,2005.
[59] J.Ho,K.-C.Lee,M.-H.Yang,et al.Visual tracking using learned linear subspaces.[C]In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Washington,DC,United States,2004,1,782-789.
[60] C.Shen,A.Van Den Hengel and M.J.Brooks.Visual tracking via efficient kernel discriminant subspace learning.[C]In:Proceedings of International Conference on Image Processing,Genova,Italy,2005,2,590-593.
[61] K.-C.Lee and D.Kriegman.Online learning of probabilistic appearance manifolds for video-based recognition and tracking.[C]In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,United States,2005,I,852-859.
[62] C.Sminchisescu and A.Jepon.Generative modeling for continuous non-linearly embedded Visual inference.[C] In:Proceeding of International Conference on Machine Learning,Banff, Canada,2004.
[63] R.T.Collins,Y.X.Liu and M.Leordeanu.Online selection of discriminative tracking features.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1631-1643.
[64] J.Wang,X.Chen and W.Gao. Online selecting discriminative tracking features using particle filter.[C]In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,United States,2005,2,1037-1042.
[65] S.Avidan. Support vector tracking.[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(8):1064-1072.
[66] S.Avidan. Ensemble tracking.[C]In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,United States,2005,2,494-501.
[67] P.Viola and M.Jones.Rapid object detection using a boosted cascade of simple features.[C]In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Kauai,HI,2001,1,511-518.
[68] H.Grabner,M.Grabner and H.Bischof.Real time tracking via on-line boosting.[C]In:British Machine Vision proceedings,2006,1,47-57.
[69] R.Van der Merwe,A.Doucet,N.de Freitas,et al. The unscented particle filter.[R] Cambridge niversity Engineering Department,August,2000.
[70] E.Wan and R.van der Merwe. The unscented Kalman Filter[R]. Cambridge University Engineering Department,August,2000.
[71]彭宁嵩,杨杰,刘志,等.Mean-Shift跟踪算法中核函数窗宽的自动选取.[J]软件学报,2005,16(9):1542-1551.
[72] J.Tu,H.Tao and T.Huang. Online updating appearance generative mixture model for Meanshift tracking.[C]In:Proceedings of Asian Conference on Computer Vision,Hyderabad,India,2006,1,694-703.
[73] R.van der Merwe.Sigma-point Kalman Filters for probabilistic inference in dynamic state-space models.[D]Oregon Health and Science University,2004.
[74]程水英,张剑云.粒子滤波综述[J].宇航学报,2008,29(4):1099-1110.
[75] Haykin S Ed. . Kalman Filtering and Neural Networks [M] . NewYork : John Wiley &Sons , 2001 : 5 - 10 , 16 - 20 , 253 - 267.
[76] Bar-Shalom Y, Li X R , Kirubarajan T. Estimation with Applications to Tracking and Navigation : Theory , Algorithms , and Software[M] .New York : John Wiley & Sons , 2001 : 373 - 374 , 200 - 217 , 381- 395.
[77] Arulampalam M S , Maskell S , Gordon N , et al . A tutorial on particle filters for online Nonlinear/non-Gaussian Bayesian tracking [ J ] .IEEE Trans. Signal Processing , 2002 , 50 (2) : 174 - 188.
[78] Chen Zhe. Bayesian Filtering : From Kalman Filters to Particle Filters , and Beyond[ R] .Hamilton : McMaster University , 2003.
[79] Uhlmann J K. Algorithm for multiple target tracking[J]American Science, 1992. 80(2):128-141
[80] Sorenson H W, Kalman Filtering: Theory and Application[M].New York: IEEE Press, 1985.
[81] Dan Simon. Optimal State Estimation : Kalman , H∞, and Nonlinear Approaches[M] . New Jersey : John Wiley &Sons , 2006 , 130 : 121- 330 , 400 - 409 , 158 - 173.
[82] Briers M, Maskell S R , Wright R. A rao-blackwellised unscented Kalman filter[C] . In : Proc.the Sixth Int . Conf . Information Fusion , Cairns , Australia , 2003 : 55 - 61.
[83] R van der Merwe , Wan E. The square-root unscented kalman filter for state and Parameter estimation[C] . In : Proceedings of the International Conference on Acoustics , Speech , and Signal Processing( ICASSP) , Salt Lake City , 2001 : 3461 - 3464.
[84] R. van der Merwe , Wan E. Efficient derivative-free Kalman filters for online learning[C]//2001 Proc. European Symposium on Artificial Neural Networks (ESANN) , Bruges , 2001 : 205 - 210.
[85]柴霖,袁建平,罗建军,等.非线性估计理论的最新进展[J].宇航学报, 2005 , 26 (3) : 380- 384.
[86]苏卡尼,吕明.插值滤波器在单站外辐射源无源定位中的应用[J] .舰船电子工程, 2006 , 26 (4) : 146 - 148 , 162.
[87] Ito K, Xiong K. Gaussian filters for nonlinear filtering problems[J ] .IEEE Trans. Automat . Contr. , 2000 , 45 (5) : 910 - 927.
[88]袁泽剑,郑南宁,贾新春.高斯-厄米特粒子滤波器[J ] .电子学报, 2003 , 31 (7) : 970 - 973.
[89]程水英,邹继伟,汤鹏.免微分非线性Bayesian滤波方法评述[J].宇航学报2009,30(3):843-859.
[90] Bergman N. Recursive Bayesian estimation-navigation and tracking applications[D]. Linkêping : Linkêping University , 1999.
[91] Carpenter J , Clifford P , Fearnhead P. Improved particle filter for nonlinear problems[J ] .IEE Proc.-Radar , Sonar Navig. , 1999 : 146(1) : 1 - 7.
[92] Marshall A. The use of multi-stage sampling schemes in Monte Carlocomputations [C]\\Meyer M. Symposium on Monte Carlo Methods.New York : 1956 : 123 - 140.
[93] Doucet A , Gordon NJ , Krishnamurthy V. Particle filters for state estimation of jump Markov linear systems[J ] . IEEE Trans. Signal Processing , 2001 , 49 (49) : 613 - 624.
[94] Kong A , Liu J S , Wong W H. Sequential imputations and Bayesian missing data problems [ J ] . J . American Statistical Association , 1994 , 89 : 278 - 288.
[95] Herman S M. A particle filtering approach to joint passive radar tracking and target classification[D] . Illinois : University of Illinois at Urbana-Champaign , 2002.
[96] Isard M, Blake A. CONDENSATION-conditional density propagation for visual tracking[J ] . Int . J . Computer Vision , 1998 , 29 (1) : 5 -28.
[97] MacCormick J , Isard M. Partitioned sampling , articulated objects ,and interface-quality hand tracking[ R] . Oxford : University of Oxford , 2000.
[98]姚剑敏.粒子滤波跟踪方法研究[D]长春:中国科学院长春光学精密机械与物理研究所.
[99] Spall J C. Estimation via markov chain monte carlo[J ] . IEEE Control Systems Magazine , 2003 , 23 (2) : 34 - 45.
[100] Chen Z, Haykin S. On different facets of regularization theory[J ] .Neural Comput .2002 14 (12) : 2791 - 2846.
[101]程水英,张剑云.裂变自举粒子滤波[J ] .电子学报, 2008 ,36 (3) : 500 - 504.
[102] Crisan D , Doucet A. A survey of convergence results on particle filtering methods forpractitioners[J ] . IEEE Trans. Signal Processing ,2002 , 50 (3) : 736 - 746.
[103] Freitas J F G, Niranjan M , Gee A H ,et al. Sequential Monte Carlo Methods to TrainNeuralNetwork Models [J] .Neural Computation , 2000 , 12 (4) : 955-993.
[104]李良群,姬红兵,罗军辉.迭代扩展卡尔曼粒子滤波器[J].西安电子科技大学学报,2007,34(2):233-238.
[105] M.Pitt and N.Shephard. Filtering via simulation:auxiliary particle filters.[J]Journal of the American statistical association,1999,94(446):590-599.
[106] H.Nait-Charif and S.J.McKenna. Tracking poorly modelled motion using particle filters with iterated likelihood weighting.[C]In:Proceedings of Asian Conference on Computer Vision Jeju Island,Korea,2004.
[107] C.H.Shen,H.Anton van den,A.Dick,et al.Enhanced importance sampling:unscented auxiliary particle filtering for visual tracking.[C]In:Proceedings of Australian Conference on Artificial Intelligence,Cairns,Australia,2004,180-191.
[108]杨峰,潘泉,等.多源信息空间配准中的UT变换采样策略研究[J].系统仿真学报,2006,18(3):713-717.
[109]程水英,毛云祥.迭代无味卡尔曼滤波器[J].数据采集与处理,2009,24(S):43-47.
[110] Julier S J , U h lmann J K, Durrant-Whyte H F. A new approach for the nonlinear transformation of means and covariances in filters and estimators[J]. IEEE Transation Automatic Control, 2000, 45(3): 477-482.
[111] Julier, S.J., The scaled unscented transformation [C]//2002 American Control Conference.Anchorage Alaska: IEEE, 2002, 6: 4555– 4559.
[112] Julier S J, Uhlmann J K. Unscented Filtering and Nonlinear Estimation [J]. Proceedings of the IEEE (S0018-9219), 2004, 92(3):401– 422.
[113] Julier S J, Uhlmann J K. A consistent, Debiased method for converting between polar and Cartesian coordinate systems [C]// Proc.AeroSense: Acquisition, Tracking and Pointing XI. Orlando, Florida:SPIE, 1997, 3086: 110–121.
[114] Julier, S.J., Uhlmann, J.K, A new extension of the Kalman filter to nonlinear systems [C]// Proc. AeroSense: 11th Int. Symp.Aerospace/Defense Sensing, Simulation and Controls. Orlando,Florida: SPIE, 1997, 3086: 182-193.
[115] Julier S J. The spherical simplex unscented transformation [C]// 2003 American Control Conference. Denver, Colorado: IEEE, 2003, 3:2430-2434.
[116] Julier S J, Uhlmann J K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations [C]// 2002 American Control Conference. Anchorage Alaska : IEEE, 2002, 2: .887– 892.
[117] PEREZ P, VERMAAK J, BLAKE A. Data fusion for visual tracking with particles [J].Proceedings of the IEEE,2004,92(3):495-513.
[118] WU PL, KONG LF, ZHAO FD, et al. Particle filter tracking based on color and sift features[C] // Audio, Language and Image Processing, 2008. Shanghai: [s. n.], 2008:932-937.
[119] F.Moreno-Noguer. Multiple cue integration for robust tracking in dynamic environments:application to video relighting.Ph.D thesis,Institute of Robotics and Industrial Informatics Technical University of Catalonia,2005.
[120] TAO X, CHRISTIAN D. Monte carlo visual tracking using color histograms and a spatially weighted oriented hausdorff measure[C]//In Proceedings of the Conference on Analysis of Images and Patterns. Groningen: Springer Berlin Heidelberg, 2003:190-197.
[121] KWOLEK B. Stereovision-based head tracking using color and ellipse fitting in a particle filter[C]// Proceedings of the 8th European Conference on Computer Vision. Prague: Springer Berlin Heidelberg, 2004:192-204.
[122] SPENGLER M, SCHIELE B. Towards robust multi-cue integration for visual tracking [J].Machine Vision and Applications,2003. 14 (1): 50–58.
[123] LOWE DG. Distinctive image features from scale-invariant keypoints [J].International Journal of Computer Vision , 2004 , 60 (2) : 91-110.
[124]管志强,陈钱,顾国华,等.基于光流直方图的云背景下低帧频小目标探测方法[J].光学学报,2009,28(8):1496-1501.
[125] LUCAS B D, KANADE T. An iterative image registration technique with an application to stereo vision[C]//Proc 7th International Joint Conf on Artificial Intelligence. Vancouver:[s. n.], 1981 :674-679.
[126] B. Ristic, S. Arulampalam, N. Gordon. Beyond the kalman filter:particle filter for tracking application[M]. Boston: Artech House,2004.
[127] N.M.Kwok, F.Gu, WZ.Zhou. Evolutionary particle filter: re-sampling from the genetic algorithm perspective[C]. Intelligent Robots and Systems 2005,Edmonton,Alberta,Canada, Aug2-6.2005, 2935-2940.
[128] Y.Zhai, M.Yeary. Implementing particle filters with metropolis-hastings algorithms[C]. Region 5 Conference: Annual Technical and Leadership Workshop, IEEE Press,April.2004, 149-152.
[129]齐欢,王小平.系统建模与仿真[M].北京:清华大学出版社, 2004.
[130] J D Hol. Resampling in Particle Filters [R]// Sweden: Link?ping University, 2004.
[131]吴宝成.粒子滤波重采样算法研究及其应用[D/OL].中国优秀硕士学位论文全文数据库, 2007, http://dlib.cnki.net/kns50.
[132] Miodrag Bolic’, Peter M Djuric’, Sangjin Hong. New Resampling Algorithms for ParticleFilters [J]. ICASSP2003 (S1845-5921), 2003,vol.2: 589-592.
[133] B.A.Berg. Markov chain monte carlo simulations and their statistical analysis[M]. Singapore:World Scientific,2004.
[134] Christian P Robert. George Casella. Monte Carlo statistical methods[M]. New York : Springer,2004.
[135] R. Y. Rubinstein and D. P. Kroese. Simulation and the Monte Carlo Method (second edition)[M]. New York: John Wiley & Sons, 2007.
[136] Christophe Andrieu, Nando de Freitas, et al. An Introduction to MCMC for Machine Learning[J]Machine Learning, 2003,Vol. 50, No. 1. pp: 5-43.
[137] M.Clerc, J.Kennedy, The particle swarm: Explosion, stability, and convergence in a multimentional complex space[J].IEEE Trans.Evol.Comput.2002,6(1): 58-73.
[138] Kennedy,J. Eberhart,R.C. Swarm Intelligence (The Morgan Kaufmann Series in Artificial Intelligence) .Morgan Kaufmann,2001.
[139] Poli, R. An analysis of publications on particle swarm optimisation applications[R]. University of Essex Department of Computer Science.2007.
[140] Trelea, I.C. The Particle Swarm Optimization Algorithm: convergence analysis and parameter selection[J]. Information Processing Letters ,2007, 85( 6):317–325.
[141] KELLY P H, KATKERE A, KURAMURA D Y, et al. An architecture for multiple perspective interactive video: proc of the 3nd ACM International Conference on Multimedia [C]// San Francisco: [ s. n. ] ,1995: 201-212.
[142] ZHOU Qu-ming, AGGARWAL J K. Object tracking in an outdoor environment using fusion of features and cameras[J].Image and Vision Computing, 2006, 24(11): 1244- 1255.
[143] XU M, ORWELL J, LOWEY L, et al. Architecture and algorithms for tracking football players with multiple cameras[J] Image and Signal Processing ,2005,152(2):232-241.
[144] JAVERD O, RASHEED Z, SHAFIQUE K, et al. Tracking across multiple cameras with isjoint views[C]//Proceedings of IEEE International Conference on Computer Vision. Nice: ACM Press, 2003:165- 171.
[145] CALDERARA S, PRATI A, VEZZANI R, et al. Consistent labeling for multicamera object tracking[J].IEEE Trans on PAMI , 2003, 25( 10) :1355- 1360.
[146] Bar-Shalom Y, Fortmann T E. Tracking and data association [M] .New York :Academic , 1988.
[147] Mori S, Chong C, Tse T, et al.. Tracking and classify in multiple targets without a priori identification. IEEE Trans. on Automatic,Control,1986,AC-31(5):401-409.
[148] Thomas E.Fortmann, Yaakov Bar-Shalom. Sonar Tracking of Multiple Targets Using JointProbabilistic Data Association [J].IEEE Journal of Oceanic Engineering (S0364-9059), 1983, 8(3): 173-184.
[149] Kuo Chu Chang, Yaakov Bar-Shalom. Joint Probabilistic DataAssociation for Multitarget Tracking with Possibly Unresolved Measurements and Maneuvers [J]. IEEE Transactions on Automatic Control(S0018-9286), 1983, 29(7): 585-594.
[150] Yaakov Bar-Shalom, X R Li. Multitarget-Multisensor Tracking:Principles and Techniques [M]. Storrs, CT: YBS, 1995.
[151] Songhwai Oh, Stuart Russell, and Shankar Sastry, Markov Chain Monte Carlo Data Association for Multi-Target Tracking[J]. IEEE Trans. On Automatic Control, 2009 ,54(3):481-491.
[152] J.A.Roecker, G.L.Phillis. Suboptimal Joint Probabilistic Data Association [J]. IEEE. Transactions on Aerospace and Electronic Systems(S0018-9251), 1993, 29(2): 510-517.
[153] K. Mikolajczyk, T. Tuytelaars,et al. A comparison of affine region detectors[J]. International Journal of Computer Vision, 2005,65(1):43-72
[154] Mikolajczyk, K. and Schmid, C. Scale & affine invariant interest point detectors[J]. International Journal on Computer Vision, 2004,60(1):63-86.
[155] K. Mikolajczyk and C. Schmid, Scale and Affine invariant interest point detectors[J]. International Journal of Computer Vision,2004, 60(1):63-86.
[156] FISCHLER MA, BOLLES RC. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography [J]. CACM, 1981,24(6):381?395.
[157] HARTLEY R. Multiple view geometry in computer vision[M]. Cambridge: Cambridge University Press,2000.
[158] BARRON J L , FLEET D J , BEAUCHEMIN S S , et al. Performance of optical flow techniques[J]. International Journal of Computer Vision, 1992,12(1):43-77
[159] MIKOLAJCZYK K, SCHMID C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27 (10) : 1615-1630.
[160] YAN KE, RAHUL SUKTHANKAR. PCA-SIFT: A more distinctive representation for local image descriptors [C]// IEEE conference on Computer Vision and Pattern Recognition. Washington: IEEE, 2004: 506-503.
[161]韩崇昭,朱洪艳,段战胜,等.多源信息融合[M] .北京:清华大学出版社,2006 .
[162] LUONG Q T, FAUGERAS O. A stability analysis of the fundamental matrix[C]//Proceedin- gs of the third European conference on Computer vision. New York: Springer Berlin Heidelberg ,1994:577-588.