视频监控系统中运动目标跟踪算法的研究
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摘要
视频监控是计算机视觉、模式识别以及人工智能等领域的一个重要的研究内容,在安全监控、智能交通、军事导航等方面有着广泛的应用前景。运动目标检测技术是视频监控系统中一个重要组成部分,其检测结果直接影响着后续的目标定位、识别和跟踪以及运动行为的理解和描述。国内外大批学者投身于该领域的研究和探索,并取得了大量的成果。本文是在这些成果的基础上,对视频监控系统中运动目标检测算法及目标跟踪进行了研究。
本文主要介绍了视频监控技术中目标检测和跟踪的基本理论和关键技术。重点研究静止背景下目标的检测和提取及运动目标跟踪等方面内容。
在运动目标检测方面,针对背景差分法,对常用的背景建模方法进行了分析研究,并提出了一种基于混合高斯模型的改进算法。传统的混合高斯模型对于方差和均值采取相同的学习率,这样不仅没有考虑到方差和均值的不同特点而且容易使方差陷入偏小。为此本文提出针对方差与均值在模型更新中的不同特点采取不同的学习更新策略。对于方差的更新采用动态的学习方法,并集合历史数据对当前帧的影响,提高了方差学习的收敛速度和精确度。仿真实验表明该方法不仅能准确的进行背景建模识别出运动目标,同时能够有效的抑制噪声。
在运动目标跟踪方面,对传统的目标跟踪算法进行分析和讨论。在高斯非线性跟踪滤波算法上针对跟踪系统中由于弱观测性,大的初始化误差使的系统出现不稳定、跟踪收敛速度慢,鲁棒性能差的问题,提出了基于观测迭代插值滤波器。该算法在插值滤波器基础上,利用观测迭代过程来取代单纯的近似条件估计进行预测,减小观测函数线性化所带来的误差影响具有更精确的状态和协方差估计性能。针对非高斯非线性化系统在基于序贯重要性抽样及贝叶斯理论的粒子滤波能够很好的处理。如何选取重要密度函数以减小粒子退化影响提高粒子滤波精度是粒子滤波的主要问题之一。本文采用基于观测迭代的插值参考分布提高重要密度函数估计精度,减少了后验概率密度估计误差,同时结合观测系统的最近一次的量测,更好的匹配后验概率密度。
在实际应用方面,针对室内运动人设计了一种运动目标跟踪系统。介绍了系统的设计思想,并根据实际情况进行了模块化设计,包括模块的工作原理和系统设计流程。
Video surveillance system is a crucial issue in the field of computer vision,pattern recognition and artificial intelligence, and it has broad application foreground for safety monitoring, intelligent transportation and military navigation. Moving object detection technique is an important component of video surveillance system. The detection results directly affect the following components, like target location, identification and tracking, as well as comprehension and description of movement behavior. Large numbers of researchers devoted themselves in the area and have already achieved many progresses. The dissertation studies moving object detection algorithm and object tracking of video surveillance system based on the current conclusion.
This paper mainly discusses the fundamental theories and key technologies of object detection and tracking for Intelligent Video Surveillance. The following topics are researched in details, such as detection and extraction of object with static background, the image segmentation measurement, tracking of moving objects and so on.
In moving object detection aspects, background subtraction is studied. After analyses and studies on usual background modeling method, the novel gaussian mixture model based the traditional one is proposed. Traditional mixed-Gaussian model takes the same rate of study for the variance and the mean, which did not take into account the variance and mean of the different features and is easy to make into a small variance. To solve the problem, the new method takes a different update strategy to the mean value and the variance according to the different characteristics of them. With the use of dynamic methods on the update of variance study, and collection of historical data on the impact of the current frame, the variance study improves convergence speed and accuracy. Simulation results show that the method can not only improve the accuracy on background modeling to identify the target, and at the same time effectively suppress noise.
As for target tracking, a new tracking algorithm is proposed based on the analysis and discussion on the traditional target tracking algorithm. It is of great importance to develop a robust and fast tracking algorithm in tracking system because of its inherent disadvantages such as weak observability and large initial errors. An improved algorithm referred to as the iterated divided difference filter is proposed based on the analysis and comparison of conventional nonlinear tracking algorithms. The algorithm predicts states by an iterated observation instead of a simple approximation, which reduces the error effects brought by observation linearization. And based on the concept of sequential importance sampling (SIS) and the use of Bayesian theory, particle filter is particularly useful in dealing with nonlinear and non-Gaussian problems. How to select an important density function to reduce the affection of the particle degeneration and improve the accuracy of the particle filter is one of the major problems. In this paper, a new particle filter is proposed that uses a iterated divided difference filter to generate the importance proposal distribution is proposed to decrease the posterior probability distribution estimation error, enhance tracking effect. The proposal distribution integrates the latest measurements into system state transition density so it can match the posterior density well.
In practical application aspects, a moving object tracking system is designed to detect and track indoor moving people. System framework and the equipment composition are given, and a modular design is presented according to actual condition, including working principle of module and system design flow.
本文主要介绍了视频监控技术中目标检测和跟踪的基本理论和关键技术。重点研究静止背景下目标的检测和提取及运动目标跟踪等方面内容。
在运动目标检测方面,针对背景差分法,对常用的背景建模方法进行了分析研究,并提出了一种基于混合高斯模型的改进算法。传统的混合高斯模型对于方差和均值采取相同的学习率,这样不仅没有考虑到方差和均值的不同特点而且容易使方差陷入偏小。为此本文提出针对方差与均值在模型更新中的不同特点采取不同的学习更新策略。对于方差的更新采用动态的学习方法,并集合历史数据对当前帧的影响,提高了方差学习的收敛速度和精确度。仿真实验表明该方法不仅能准确的进行背景建模识别出运动目标,同时能够有效的抑制噪声。
在运动目标跟踪方面,对传统的目标跟踪算法进行分析和讨论。在高斯非线性跟踪滤波算法上针对跟踪系统中由于弱观测性,大的初始化误差使的系统出现不稳定、跟踪收敛速度慢,鲁棒性能差的问题,提出了基于观测迭代插值滤波器。该算法在插值滤波器基础上,利用观测迭代过程来取代单纯的近似条件估计进行预测,减小观测函数线性化所带来的误差影响具有更精确的状态和协方差估计性能。针对非高斯非线性化系统在基于序贯重要性抽样及贝叶斯理论的粒子滤波能够很好的处理。如何选取重要密度函数以减小粒子退化影响提高粒子滤波精度是粒子滤波的主要问题之一。本文采用基于观测迭代的插值参考分布提高重要密度函数估计精度,减少了后验概率密度估计误差,同时结合观测系统的最近一次的量测,更好的匹配后验概率密度。
在实际应用方面,针对室内运动人设计了一种运动目标跟踪系统。介绍了系统的设计思想,并根据实际情况进行了模块化设计,包括模块的工作原理和系统设计流程。
Video surveillance system is a crucial issue in the field of computer vision,pattern recognition and artificial intelligence, and it has broad application foreground for safety monitoring, intelligent transportation and military navigation. Moving object detection technique is an important component of video surveillance system. The detection results directly affect the following components, like target location, identification and tracking, as well as comprehension and description of movement behavior. Large numbers of researchers devoted themselves in the area and have already achieved many progresses. The dissertation studies moving object detection algorithm and object tracking of video surveillance system based on the current conclusion.
This paper mainly discusses the fundamental theories and key technologies of object detection and tracking for Intelligent Video Surveillance. The following topics are researched in details, such as detection and extraction of object with static background, the image segmentation measurement, tracking of moving objects and so on.
In moving object detection aspects, background subtraction is studied. After analyses and studies on usual background modeling method, the novel gaussian mixture model based the traditional one is proposed. Traditional mixed-Gaussian model takes the same rate of study for the variance and the mean, which did not take into account the variance and mean of the different features and is easy to make into a small variance. To solve the problem, the new method takes a different update strategy to the mean value and the variance according to the different characteristics of them. With the use of dynamic methods on the update of variance study, and collection of historical data on the impact of the current frame, the variance study improves convergence speed and accuracy. Simulation results show that the method can not only improve the accuracy on background modeling to identify the target, and at the same time effectively suppress noise.
As for target tracking, a new tracking algorithm is proposed based on the analysis and discussion on the traditional target tracking algorithm. It is of great importance to develop a robust and fast tracking algorithm in tracking system because of its inherent disadvantages such as weak observability and large initial errors. An improved algorithm referred to as the iterated divided difference filter is proposed based on the analysis and comparison of conventional nonlinear tracking algorithms. The algorithm predicts states by an iterated observation instead of a simple approximation, which reduces the error effects brought by observation linearization. And based on the concept of sequential importance sampling (SIS) and the use of Bayesian theory, particle filter is particularly useful in dealing with nonlinear and non-Gaussian problems. How to select an important density function to reduce the affection of the particle degeneration and improve the accuracy of the particle filter is one of the major problems. In this paper, a new particle filter is proposed that uses a iterated divided difference filter to generate the importance proposal distribution is proposed to decrease the posterior probability distribution estimation error, enhance tracking effect. The proposal distribution integrates the latest measurements into system state transition density so it can match the posterior density well.
In practical application aspects, a moving object tracking system is designed to detect and track indoor moving people. System framework and the equipment composition are given, and a modular design is presented according to actual condition, including working principle of module and system design flow.
引文
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8. P.S. Pa. Design of a modular assembly of four-footed robots with multiple functions[J]. Robotics and Computer-Integrated Manufacturing, 2009,25(4-5): 804-809.
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2.杨明,王汝琳.智能视频监控技术解析[EB/OL].http://www.cps.com.cn/news/Html/ saftcity/fenxi/2007/9/334471631.html, 2007-9-29
3.迪克斯著.人机交互[M].第三版.蔡利栋译.北京:电子工业出版社, 2006.
4. O. Akanyeti, U. Nehmzow, S.A. Billings. Robot training using system identification [J]. Robotics and Autonomous Systems, 2008,56(12):1027-1041
5. Nori Francesco, Natale Lorenzo, Sandini Giulio, Metta Giorqio. Autonomous learning of 3D reaching in humanoid robot[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007: 1142-1147.
6. Narayan Srinivasa, Stephen Grossberg. A head–neck–eye system that learns fault-tolerant saccades to 3-D targets using a self-organizing neural model[J]. Neural Networks, 2008, 21(9): 1380-1391.
7. J. González, J.L. Blanco, C. Galindo, A. Ortiz-de-Galisteo, J.A. Fernández-Madrigal, F.A. Moreno, J.L. Martínez. Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach[J]. Robotics and Autonomous Systems, 2009,57(5): 496-507.
8. P.S. Pa. Design of a modular assembly of four-footed robots with multiple functions[J]. Robotics and Computer-Integrated Manufacturing, 2009,25(4-5): 804-809.
9. Francisco Martín, Vicente Matellán, Pablo Barrera, JoséM. Ca?as. Localization of legged robots combining a fuzzy-Markov method and a population of extended Kalman filters[J]. Robotics and Autonomous Systems, 2007,55(12): 870-880.
10. Ayache N., Cohen I., and Herlin I.. Medical Image Tracking[M]. In: Active Vision, Chapter 20,MIT Presss,1992.
11. Marais P., Guillemaud R., Sakuma M., Zisserman A., and Brady M.. Visualsing Cerebral Asymmetry[J]. Lecture Notes in Computer Science, 1996:411-416.
12. Kanade T,Collins R,Lipton A,et al.Advances in cooperative multi-sensor video surveill- ance[C].Proceedings DARPA Image Understanding Workshop. Morgan Kaufmann: DARPA,1998:3-4.
13. ITU-T Recommendation H.264 (03/2005).Advanced video coding for generic audiovisual service[S],2005.
14. Haritaoglu I,Harwood D,Davis L S.W4:Real-time surveillance of people and their activities[J].IEEE Transactions Pattern Analysis and Machine Intelligence,2000,22(8): 809-830.
15. Maggioni C and Kammerer B.Gesture Computer: History, Design, and Applications[N]. Computer Vision for Human-Machine Interaction. Cambridge University Press,1998.
16. Freeman W and Weissman C. Television control by hand gestures[C].In:Proceedings of International Conference on Automatic Face and Gesture Recognition(FGR).Zurich, Switzerland:FGR,1995:179-183.
17. NAYLOR M, ATTWOOD C I. Annotated digital video of intelligent surveillance and optimized retrieval : final report[R]. ADVISOR Conortium,2003.
18.菅云峰.智能视觉监控技术的研究背景现状及展望[EB/OL].http://www.china-vision. net/technology/intergrad/200712/20410.html,2007-12-20.
19. Adrees Ahmad, Mahbub Gani, Fuwen Yang. Decentralized robust Kalman filtering for uncertain stochastic systems over heterogeneous sensor networks [J]. Signal Processing, 2008, 88(8): 1919-1928.
20. Panornruttanarug, B. Longman, R.W. Using Kalman filter to attenuate noise in learning and repetitive control can easily degrade performance[C]. SICE Annual Conference,2008: 3453-3458.
21. Ning ma. Bouchard, M. Goubran, R.A. Speech enhancement using a masking threshold constrained kalman filter and its heuristic implementations[C]. IEEE Transactions. Audio, Speed, and Language Processing. 2006,14(1):19-32.
22. Yuan Gao, Gui-Li Tao, Zi-Li Deng. Decoupled distributed Kalman fuser for descriptor systems[J]. Signal Processing, 2008,88(5): 1261-1270.
23. Nilanjan Saha, and D. Roy. Extended Kalman filters using explicit and derivative-free local linearizations [J]. Applied Mathematical Modelling, 2009, 33(6): 2545-2563.
24. Agnieszka Krok, Zenon Waszczyszyn. Kalman filtering for neural prediction of response spectra from mining tremors[J]. Computers & Structures, 2007,85(15-16): 1257-1263.
25. Terejanu, G., Singla, P., Singh, T.,Scott, P.D.. A novel Gaussian Sum Filter Method for accurate solution to the nonlinear filtering problem [C].International Conference on Information Fusion. 2008:1-8.
26. Julier S J, Uhlmann J K. A new approach for filtering nonlinear system[C]. Proc of the American Control Conf. Washington: Seattle, 1995: 1628-1632.
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