多摄像头多目标跟踪技术研究
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摘要
本文所说的目标跟踪(Target Tracking),指的是视觉目标跟踪(visual tracking),它的任务是研究如何从连续的视频图像中分析感兴趣目标的运动状态。目标跟踪是当前计算机视觉领域的一个重要且活跃的研究课题,是一项包括模式识别、图像处理、概率与统计推理、随机过程、系统状态估计等多门学科交叉的前沿课题。
随着目标跟踪技术的不断发展,出现了很多成功地应用于实际生活的方方面面的目标跟踪系统。同时由于实际需求,如何进行多目标跟踪成为迫切需要,于是出现了多目标跟踪(Multiple Target Tracking,MTT)的概念。多目标跟踪已经成为视觉跟踪研究课题中的热点和难点。
在大多数情况下,由于摄像头自身的视野限制,故而不可能通过单个摄像头观测整个兴趣区域。因此,为了监控宽阔区域的运动目标,人们需要一个多摄像头分布式目标监控系统。
显然,多摄像头多目标跟踪技术无论是在在学术研究领域,还是在工程应用中都具有非常重要的意义。本文主要研究了多摄像头多目标跟踪课题中的三项关键技术:目标检测、目标跟踪和多摄像头协同。概括起来主要包含以下三个方面的研究和创新:
(1)目标检测技术。介绍了当前目标检测技术的发展状况,并重点分析了混合高斯模型(GMM)背景建模目标检测技术与核密度估计前景背景建模目标检测技术的优缺点,在此基础上,提出了一种基于时空域和运动特征的目标检测技术,实验结果证明,该技术具有抗噪性强且速度快的优点。
(2)多目标跟踪技术。分析了多目标跟踪相对于单目标跟踪的难点,提出了采用联合状态的粒子滤波器的多目标跟踪算法;然后介绍了在联合状态的粒子滤波跟踪框架下,如何利用马尔可夫链蒙特卡罗(MCMC)采样方法进行粒子采样,从而保证跟踪的实时性,以及如何利用马尔可夫随机场(MRF)模型和外观模型处理多目标跟踪时出现的目标遮挡问题。相关的实验数据证明了在联合状态的粒子滤波框架下,利用上述方法可以较好地解决多目标跟踪问题中的实时性和目标遮挡问题。
(3)多摄像头协同技术。介绍了多摄像头间的目标位置映射方法,提出了一种利用SIFT特征提取算法和虚拟摄像机进行自动提取标定点的算法;然后介绍了多摄像头间目标一致性匹配问题;最后给出了一种有效的多摄像头协同分配算法。
The concept target tracking stated in this paper, which exactly means visual target tracking, has the main task to study how to analyze the interesting target's movement status from continuous images or videos. Target tracking is one of the important and hot issues in computer vision. It is an active research topic which concerns pattern recognition, image processing, probability theory and statistical inference, random process, and system state estimation.
With the development of target tracking technology, there have been many successful target tracking systems applied to all aspects of real life. Meanwhile, due to the actual needs, how to track multiple targets is an urgent need, so the concept multi-targets tracking (MTT) comes into our vision. Multi-targets tracking has already become one of the hot and
ifficult issues in visual target tracking research.
In most cases, due to the limitation of camera's vision, the supervision system can not observe the entire interesting region through a single camera. In order to monitor a vaster region, what we need is a distributed supervision system using multiple cameras.
Obviously, multi-targets tracking via multi-cameras technology is significant not only in academic research, but also in engineering applications .
This paper studies three key technologies of the task of multi-target tracking via multi-cameras: target detecting, multi-targets tracking and multi-cameras coordination. My work can be summed up as follows:
(1) Target detecting. First, I described the current status of target detecting technology, and focused on analyzing the advantages and disadvantages of the gaussian mixture model (GMM) and kernel density estimation(KDE) algorithm. Then I proposed a target detecting algorithm based on time-space and movement features, which has the advantages of strong anti-noise and speed.
(2) Multi-targets tracking. First, I analyzed the difficult aspects of multi-targets tracking compared with single-target tracking, and proposed a particle filter algorithm based joint state; Then I introduced how to use Markov chain Monte Carlo (MCMC) in the particle filter framework to ensure real-time tracking, as well as how to use Markov Random Field (MRF) model and the appearance model to handle occlusions.
(3) Multi-cameras coordination. First, I Introduced the method for mapping the targets' position between multiple cameras, and proposed an algorithm for detecting the calibration points automatically by using SIFT and virtual camera. Then, I introduced how to match the same targets between different cameras. At last, an algorithm for coordinating and assigning cameras was proposed.
随着目标跟踪技术的不断发展,出现了很多成功地应用于实际生活的方方面面的目标跟踪系统。同时由于实际需求,如何进行多目标跟踪成为迫切需要,于是出现了多目标跟踪(Multiple Target Tracking,MTT)的概念。多目标跟踪已经成为视觉跟踪研究课题中的热点和难点。
在大多数情况下,由于摄像头自身的视野限制,故而不可能通过单个摄像头观测整个兴趣区域。因此,为了监控宽阔区域的运动目标,人们需要一个多摄像头分布式目标监控系统。
显然,多摄像头多目标跟踪技术无论是在在学术研究领域,还是在工程应用中都具有非常重要的意义。本文主要研究了多摄像头多目标跟踪课题中的三项关键技术:目标检测、目标跟踪和多摄像头协同。概括起来主要包含以下三个方面的研究和创新:
(1)目标检测技术。介绍了当前目标检测技术的发展状况,并重点分析了混合高斯模型(GMM)背景建模目标检测技术与核密度估计前景背景建模目标检测技术的优缺点,在此基础上,提出了一种基于时空域和运动特征的目标检测技术,实验结果证明,该技术具有抗噪性强且速度快的优点。
(2)多目标跟踪技术。分析了多目标跟踪相对于单目标跟踪的难点,提出了采用联合状态的粒子滤波器的多目标跟踪算法;然后介绍了在联合状态的粒子滤波跟踪框架下,如何利用马尔可夫链蒙特卡罗(MCMC)采样方法进行粒子采样,从而保证跟踪的实时性,以及如何利用马尔可夫随机场(MRF)模型和外观模型处理多目标跟踪时出现的目标遮挡问题。相关的实验数据证明了在联合状态的粒子滤波框架下,利用上述方法可以较好地解决多目标跟踪问题中的实时性和目标遮挡问题。
(3)多摄像头协同技术。介绍了多摄像头间的目标位置映射方法,提出了一种利用SIFT特征提取算法和虚拟摄像机进行自动提取标定点的算法;然后介绍了多摄像头间目标一致性匹配问题;最后给出了一种有效的多摄像头协同分配算法。
The concept target tracking stated in this paper, which exactly means visual target tracking, has the main task to study how to analyze the interesting target's movement status from continuous images or videos. Target tracking is one of the important and hot issues in computer vision. It is an active research topic which concerns pattern recognition, image processing, probability theory and statistical inference, random process, and system state estimation.
With the development of target tracking technology, there have been many successful target tracking systems applied to all aspects of real life. Meanwhile, due to the actual needs, how to track multiple targets is an urgent need, so the concept multi-targets tracking (MTT) comes into our vision. Multi-targets tracking has already become one of the hot and
ifficult issues in visual target tracking research.
In most cases, due to the limitation of camera's vision, the supervision system can not observe the entire interesting region through a single camera. In order to monitor a vaster region, what we need is a distributed supervision system using multiple cameras.
Obviously, multi-targets tracking via multi-cameras technology is significant not only in academic research, but also in engineering applications .
This paper studies three key technologies of the task of multi-target tracking via multi-cameras: target detecting, multi-targets tracking and multi-cameras coordination. My work can be summed up as follows:
(1) Target detecting. First, I described the current status of target detecting technology, and focused on analyzing the advantages and disadvantages of the gaussian mixture model (GMM) and kernel density estimation(KDE) algorithm. Then I proposed a target detecting algorithm based on time-space and movement features, which has the advantages of strong anti-noise and speed.
(2) Multi-targets tracking. First, I analyzed the difficult aspects of multi-targets tracking compared with single-target tracking, and proposed a particle filter algorithm based joint state; Then I introduced how to use Markov chain Monte Carlo (MCMC) in the particle filter framework to ensure real-time tracking, as well as how to use Markov Random Field (MRF) model and the appearance model to handle occlusions.
(3) Multi-cameras coordination. First, I Introduced the method for mapping the targets' position between multiple cameras, and proposed an algorithm for detecting the calibration points automatically by using SIFT and virtual camera. Then, I introduced how to match the same targets between different cameras. At last, an algorithm for coordinating and assigning cameras was proposed.
引文
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[2]刘隆和编著,多模复合寻的制导技术,北京:国防工业出版社, 1998.
[3] A. Yilmaz, K. Shafique, M. Shah. Target tracking in airborne forward looking infrared imagery.Image and Vision Computing. 2003, 21, 623-635.
[4] B. Coifman, D. Beymer, P. Mclauchlan, J. Malik. A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C, 6, 271-288.
[5] D. Magee. Tracking multiple vehicles using foreground, background and motion models. Image and Vision Computing, 2004, 22, 143-155.
[6] Collins R.T., Lipton A.J., Kanade T., A system for video surveillance and monitoring, Proceedings of the American Nuclear Society (ANS) 8th International Topical Meeting on Robotic and Remote Systems, 1999.
[7] Haritaoglu I., Harwood D., Davis L.S., W4: Real-time surveillance of people and their activities, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22 (8), pp. 809-830.
[8] Grimson W.E.L., Stauffer C., Romano R., Lee L., Using adaptive tracking to classify and monitor activities in a site, In Proceedings of IEEE Computer Vision and Pattern Recognition, 1998, pp. 22-29.
[9] M. J. Black and A. D. Jepson. EigenTracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision, 1998, 26, 63-84.
[10] N. P. Papanikolopoulos, P. K. Khosla, T. Kanade. Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision. IEEE Transactions on Robotics and Automation, 1993, 9, 14-35.
[11] B. Jung, G. S. Sukhatme. A generalized region-based approach for multi-target tracking in outdoor environments. Proceedings of IEEE International Conference on Robotics and Automation, 2004, 2189-2195.
[12] B. Jung, G. S. Sukhatme. Detecting moving objects using a single camera on a mobile robot in an outdoor environment. Proceedings of International Conference on Intelligent Autonomous Systems, 2004, 980–987.
[13] H. Nait-Charif, S. J. McKenna. Head tracking and action recognition in a smart meeting room. Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2003, 24-31.
[14] J. MacCormick, A. Blake. A probabilistic exclusion principle for tracking multiple objects. International Journal of Computer Vision, 2000, 39, 57-71.
[15] Wax N. Signal-to-noise improvement and the statistics of tracking populations [J].Journal of Applied Physics. 1955, 26(10):586-595.
[16] Sittler R W. An optimal data association problem in surveillance theory. IEEE Transactionson Military Electronics. 1964, 8(2):125-139.
[17] Jaffer A J.,Bar-Shalom Y. On optimal tracking in multiple target environments. Proceedings of the third Symposium on Non-Linear Estimation Theory and Its Applications, San Diego: 1972, 112-117.
[18] Singer R A.,Stein J J. An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance systems. Proceedings of the 1971 IEEE Conference on Decision and Control, Miami Beach: 1971, 171-175.
[19] Doucet, A., de Freitas, J.F.G., Gordon N.J. Sequential Monte Carlo methods in practice. New York: Springer-Verlag, 2001.
[20] Isard M., Blake A. CONDENSATION Conditional density propagation for visual tracking. International Journal of Computer Vision, 1998, 29 (1), pp. 5-28.
[21]Gordon N.J., Salmond D.J., Smith A.F.M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 1993, 140 (2), pp. 107-113.
[22] Kitagawa G., Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 1996, 5(1), pp. 1-25.
[23]侯志强,韩崇昭.视觉跟踪技术综述A Survey of Visual Tracking [自动化学报Acta Automatica Sinica], 2006年04期.
[24] Y. Sheikh and M. Shah, Bayesian modeling of dynamic scenes for object detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 27(11), 1778-1792(2005)
[25] Y. Sheikh and M. Shah, Bayesian object detection in dynamic scenes, IEEE Conf. Computer Vision and Pattern Recognition, 1, 74-79 (2005)
[26] A. Mittal and N. Paragios, Motion-based background subtraction using adaptive kernel density estimation, IEEE Conf. Computer Vision and Pattern Recognition, 2, 302-309(2004)
[27] A. Elgammal, R. Duraiswami, D. Harwood and L. S. Davis, Background and foreground modeling using non-parametric kernel density estimation for visual surveillence, Proc. IEEE, 90, 1151-1163(2002)
[28] I. Haritaoglu, D. Harwood and L. S. Davis, W4: Real-time surveillance of people and their activities, IEEE Trans. Pattern Analysis and Machine Intelligence, 22(8), 809-830(2000)
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