基于均值漂移和粒子滤波的目标跟踪算法研究
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摘要
在计算机视觉领域,基于帧间的视频图像目标跟踪已经成为一个热门课题,广泛应用于机动目标跟踪、机器人研究、人机接口和图像目标编码等。而要在目标快速移动、遮挡、目标变形、光照变化、背景噪声以及有实时性要求等条件下也能实现稳健的目标跟踪是学者们关注的焦点,也是目前实际应用中一个急待解决的难题。
在众多的目标跟踪算法中,作者深入研究了均值漂移和粒子滤波算法。均值漂移算法是一种基于密度梯度上升的非参数方法,通过迭代运算找到目标位置,实现目标跟踪。它显著的优点是算法计算量小,简单易实现,很适合于实时跟踪场合;但是跟踪小目标和快速移动目标时常常失败,而且在全部遮挡情况下不能自我恢复跟踪。本文在第二章对均值漂移算法理论和在目标跟踪的应用作了详细的推导和描述,通过大量实验提出应用核直方图来计算目标分布,证明了均值漂移算法具有很好的实时性特点。在第三、四章节中对均值漂移算法缺点作了实验分析。
另外一种引人注意的方法是粒子滤波算法,得益于它在充满噪声的复杂环境也能表现出优异的跟踪性能。粒子滤波器是一种基于传播样本集的递归贝叶斯滤波器,同时它保持多重假设以及使用随机运动模型预测目标位置。拥有多重假设使得跟踪器能很好的处理背景中的噪声影响,并在短时丢失目标或者跟踪失败的情况下能自我恢复跟踪,在非线性非高斯场合下能实现稳健的跟踪。然而这种方法有两个关键问题:退化现象和很大计算量,在实时跟踪系统中构成了应用瓶颈。本文在第三章中对粒子滤波算法原理以及在目标跟踪中的应用作了详细讨论,设计了目标模板更新方法。实验证明,与均值漂移跟踪算法相比,粒子滤波跟踪算法具有很好的鲁棒性和抗遮挡、抗干扰性,但是计算量却很大。
本文中,作者结合两种算法的优缺点提出了一条新的思路,就是将均值漂移嵌入到粒子滤波算法里面,对粒子样本进行聚集作用,能得到更多具有高权重的样本粒子,丢弃那些对跟踪贡献几乎为零的样本,同时大量减少了用于描述目标状态的样本数量。文中第四章详细阐述设计了均值漂移嵌入粒子滤波跟踪算法,实验证明这种方法解决了粒子滤波器的退化现象和计量大的问题,在跟踪同一目标实验中,这种算法不仅保持了粒子滤波算法的高鲁棒性和抗遮挡、抗干扰性,而且算法计算量不到粒子滤波算法的三分之一,使算法的实时性大大提升。
Tracking visual objects through image frames has been a hot topic in computer vision field and is widely applied to surveillance, robotics, human machine interface, object based video coding, etc. However, the task of robust tracking is a focus concerned by scholars that regarding fast motion, occlusion, object deformation, illumination variation, background clutters, real-time restriction, etc. It is also a difficult problem must be solved in practice using currently.
The author has deeply researched the algorithms of mean shift and particle filter among many object tracking algorithms. The algorithm of mean shift is a non-parametric method based upon climbing gradient. It searches object by iteration to realize object tracking. The obvious merit of mean shift is lesser amount of calculation, simple and easy to realize, so it can meet the need of real-time tracking. But it fails in tracking small and fast moving targets and in recovering a track after a total occlusion. In second chapter, the algorithm of mean shift theory and its using in object tracking are deduced and described in detail. An algorithm of kernel histogram to calculate the distribution of target is proposed through a large number of experiments. The experiments results also show the algorithm is real-time. The analysis of drawbacks of the algorithm of mean shift is in the experiments of the third and fourth chapter.
Another algorithm have attracted much attention is particle filter, due to its robust tracking performance in cluttered environments. The particle filter is used to apply a recursive Bayesian filter based on the propagation of sample set over time, maintain multiple hypotheses at the same time and use a stochastic motion model to predict the position of the object. Maintaining multiple hypotheses allows the tracker to handle clutter in the background, and recover from failure or temporary distraction. It has been proved to be a robust method of tracking in non-linear and non-Gaussian case. However, two common problems of the particle filter technique are the degeneracy phenomenon and the huge computational cost. Thus, those problems constitute a bottleneck to the application of particle filter to real-time tracking systems. The algorithm of particle filter theory and its using in object tracking are discussed in detail in third chapter. The method of target template update is put forward. The experiments results show that the algorithm of particle filter is much more robust and much better performance of resisting occlusion and disturbance compare with the algorithm of mean shift. But its computational cost is very huge.
In this paper, the author combines the merits and drawbacks of two algorithms and proposes a new idea that is an algorithm uses the mean shift algorithm inside the particle filter. With the help of the mean shift algorithm, we can sample more particles of higher weights, and discard those particles whose contribution to the tracking is almost zero. At the mean time, it reduces the mount of samples that represent the states of the object. The experiments results show the new algorithm reduces the degeneracy problem and the computational cost of the particle filter. In the experiment of tracking one same object, this tracking algorithm not only maintain the high robustness and better performance of resisting occlusion and disturbance of the algorithm of particle filter, but also the computational cost is less than one third of the algorithm of particle filter's. The real-time characteristic of this algorithm increases dramatically.
在众多的目标跟踪算法中,作者深入研究了均值漂移和粒子滤波算法。均值漂移算法是一种基于密度梯度上升的非参数方法,通过迭代运算找到目标位置,实现目标跟踪。它显著的优点是算法计算量小,简单易实现,很适合于实时跟踪场合;但是跟踪小目标和快速移动目标时常常失败,而且在全部遮挡情况下不能自我恢复跟踪。本文在第二章对均值漂移算法理论和在目标跟踪的应用作了详细的推导和描述,通过大量实验提出应用核直方图来计算目标分布,证明了均值漂移算法具有很好的实时性特点。在第三、四章节中对均值漂移算法缺点作了实验分析。
另外一种引人注意的方法是粒子滤波算法,得益于它在充满噪声的复杂环境也能表现出优异的跟踪性能。粒子滤波器是一种基于传播样本集的递归贝叶斯滤波器,同时它保持多重假设以及使用随机运动模型预测目标位置。拥有多重假设使得跟踪器能很好的处理背景中的噪声影响,并在短时丢失目标或者跟踪失败的情况下能自我恢复跟踪,在非线性非高斯场合下能实现稳健的跟踪。然而这种方法有两个关键问题:退化现象和很大计算量,在实时跟踪系统中构成了应用瓶颈。本文在第三章中对粒子滤波算法原理以及在目标跟踪中的应用作了详细讨论,设计了目标模板更新方法。实验证明,与均值漂移跟踪算法相比,粒子滤波跟踪算法具有很好的鲁棒性和抗遮挡、抗干扰性,但是计算量却很大。
本文中,作者结合两种算法的优缺点提出了一条新的思路,就是将均值漂移嵌入到粒子滤波算法里面,对粒子样本进行聚集作用,能得到更多具有高权重的样本粒子,丢弃那些对跟踪贡献几乎为零的样本,同时大量减少了用于描述目标状态的样本数量。文中第四章详细阐述设计了均值漂移嵌入粒子滤波跟踪算法,实验证明这种方法解决了粒子滤波器的退化现象和计量大的问题,在跟踪同一目标实验中,这种算法不仅保持了粒子滤波算法的高鲁棒性和抗遮挡、抗干扰性,而且算法计算量不到粒子滤波算法的三分之一,使算法的实时性大大提升。
Tracking visual objects through image frames has been a hot topic in computer vision field and is widely applied to surveillance, robotics, human machine interface, object based video coding, etc. However, the task of robust tracking is a focus concerned by scholars that regarding fast motion, occlusion, object deformation, illumination variation, background clutters, real-time restriction, etc. It is also a difficult problem must be solved in practice using currently.
The author has deeply researched the algorithms of mean shift and particle filter among many object tracking algorithms. The algorithm of mean shift is a non-parametric method based upon climbing gradient. It searches object by iteration to realize object tracking. The obvious merit of mean shift is lesser amount of calculation, simple and easy to realize, so it can meet the need of real-time tracking. But it fails in tracking small and fast moving targets and in recovering a track after a total occlusion. In second chapter, the algorithm of mean shift theory and its using in object tracking are deduced and described in detail. An algorithm of kernel histogram to calculate the distribution of target is proposed through a large number of experiments. The experiments results also show the algorithm is real-time. The analysis of drawbacks of the algorithm of mean shift is in the experiments of the third and fourth chapter.
Another algorithm have attracted much attention is particle filter, due to its robust tracking performance in cluttered environments. The particle filter is used to apply a recursive Bayesian filter based on the propagation of sample set over time, maintain multiple hypotheses at the same time and use a stochastic motion model to predict the position of the object. Maintaining multiple hypotheses allows the tracker to handle clutter in the background, and recover from failure or temporary distraction. It has been proved to be a robust method of tracking in non-linear and non-Gaussian case. However, two common problems of the particle filter technique are the degeneracy phenomenon and the huge computational cost. Thus, those problems constitute a bottleneck to the application of particle filter to real-time tracking systems. The algorithm of particle filter theory and its using in object tracking are discussed in detail in third chapter. The method of target template update is put forward. The experiments results show that the algorithm of particle filter is much more robust and much better performance of resisting occlusion and disturbance compare with the algorithm of mean shift. But its computational cost is very huge.
In this paper, the author combines the merits and drawbacks of two algorithms and proposes a new idea that is an algorithm uses the mean shift algorithm inside the particle filter. With the help of the mean shift algorithm, we can sample more particles of higher weights, and discard those particles whose contribution to the tracking is almost zero. At the mean time, it reduces the mount of samples that represent the states of the object. The experiments results show the new algorithm reduces the degeneracy problem and the computational cost of the particle filter. In the experiment of tracking one same object, this tracking algorithm not only maintain the high robustness and better performance of resisting occlusion and disturbance of the algorithm of particle filter, but also the computational cost is less than one third of the algorithm of particle filter's. The real-time characteristic of this algorithm increases dramatically.
引文
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[2]Cheng Y.mean shift,made seeking,and clustering.IEEE Transactions on pattern Analysis and Machine Intelligence,1995,17(8).
[3]Comaniciu D,Ramesh V,Meer P.Real-Time Tracking of Non-Rigid Objects using using Mean Shift.Computer Vision and Pattern Recognition,2000.Proceedings.IEEE Conference on Volume 2,13-15 June 2000 Page(s):142-149 vol.2.
[4]Comaniciu D and P Meer.Mean Shift:A Robust application toward feature space analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence 24.Page(s):603-619.
[5]Comaniciu D,Ramesh V,Meer P.The Variable bandwidth Mean Shift and data-driven scale selection.Computer Vision,2001.ICCV 2001.Proceedings.Eighth IEEE International Conference on Volume 1,7-14 July 2001 Page(s):438-445 vol.1
[6]Yang C,R Duraaiswami and L Davis.Similarity Measure for Nonparametric Kernel Density Based Object Tracking in submitted to NIPS 2004.2004.
[7]Collins R.T.Mean Shift Blob Tracking through Scale Space.IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2003.
[8]D Comaniciu,P Meer.Mean Shift Analysis and Applications[J],IEEE intel Conf,Comp.Vis,Kekyra,Greece,1197-1203,1999.
[9]李乡儒,吴福朝,胡占义.均值漂移算法的收敛性.软件学报,Vol.16,No.3.2005.
[10]A.Djouadi,O Snorrason,and F Garber.The Quality of Training-Sample Estimates of the Bhattacharyya Coefficient.IEEE Trans.Pattern Analysis and Machine Intelligence,vol.12,pp.92-97,1990.
[11]T Kailath.The Divergence and Bhattacharyya Distance Measures in Signal Selection.IEEE Trans.Colnm.Technology,vol.15,pp.52-60,1967.
[12]Dorin Comaniciu,Visvanathan Ramesh and Peter Meer.Kernel-Based Object Tracking.IEEE Transactions on pattern Analysis and Machine Intelligence,vol.25.no.5pp.564-577,2003.
[13]M Sanjeev Arulampalam,Simon Maskell,Neil Gordon.A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J].IEEE Transactions on Signal Processing,2002,50(2):174-188.
[14]D Crisan and A Doucet.A Survey of convergence results on particle filtering methods for practitioners[J].IEEE Trans.Speech and Audio Proc.,2002,10(3):173-185.
[15]Andrew D.Bagdanov,Alberto Del Bimbo.Adaptive uncertainty estimation for particle filter-based trackers.IEEE 14th International Conference on Image Analysis Processing(ICIAP 2007)0-7695-2877-5/07.
[16]Katja Nummiaro,Esther Koller-Meier,Luc Van Gool.An adaptive color-based particle filter.Image and Vision Computing 21(2003)99-110.
[17]Jung Uk Cho,Seung Hun Jin,Jong Eun Byun.A Real-Time Object Tracking System Using a Particle Filter.IEEE/RSJ International Conference on Intelligent Robots and Systems October 9-15.2006 2822-2827.
[18]Ling Wu,Zhidong Deng and Peifa Jia.A Post-Resampling Based Particle Filter for Online Bayesian Estimation and Tracking.IEEE.Proeeding of the 6th World Congress on Intelligent Control and Automation,June 21-23,2006,Dalian,China.4331-4334.
[19]Cheng Chang.Kernel Particle Filter for Visual Tracking.IEEE SIGNAL PROCESSING LETTERS.VOL,12,NO,3,MARCH 2005 242-245.
[20]Mukesh A.Zaveri,S.N.Merchant,Uday B Desai.Arbitrary Trajectories Tracking using Multiple Model Based Particle Filtering in Infrared Image Sequence.IEEE.Proceedings of the International Conference on Information Technology:Coding and Computing(ITCC'04).
[21]裴鹿成,张孝泽.蒙特卡罗方法及其在粒子输送问题中的应用.北京科学出版社.1980.
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