全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

自助重要性采样用于实时多目标视觉跟踪

DOI: 10.3724/SP.J.1004.2012.01663, PP. 1663-1670

Keywords: 多目标跟踪,视觉跟踪,粒子滤波,马尔可夫随机场,自助法,重要性采样

Full-Text   Cite this paper   Add to My Lib

Abstract:

?多目标视觉跟踪的主要困难来自于多个目标交互(部分或完全遮挡)导致的歧义性.马尔可夫随机场(Markovrandomfield,MRF)可以消除这种歧义性且无需显式的数据关联.但是,通用概率推理算法的计算代价很高.针对上述问题,本文做出了3点贡献:1)设计了新的具有"分散-集中-分散"结构的递归贝叶斯跟踪框架—自助重要性采样粒子滤波器,它使用融入当前时刻观测的重要性密度函数解决维数灾难问题,将计算复杂度从指数增长变为线性增长;2)提出了新的蒙特卡洛策略—自助重要性采样,利用MRF的因子分解性质进行重要性采样,并使用自助法产生低成本高质量的样本、降低似然度计算次数和维持多模式分布;3)采用了新的边缘化技术—使用辅助变量采样进行边缘化,使用自助直方图对边缘后验分布进行密度估计.实验结果表明,本文提出的算法能够对大量目标进行实时跟踪,能够处理目标间复杂的交互,能够在目标消失后维持多模式分布.

References

[1]  Oh S, Russell S, Sastry S. Markov chain Monte Carlo data association for multi-target tracking. IEEE Transactions on Automatic Control, 2009, 54(3): 481-497
[2]  Chang C, Ansari R, Khokhar A. Multiple object tracking with kernel particle filter. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 566-573
[3]  Okuma K, Taleghani A, Freitas N D, Little J J, Lowe D G. A Boosted Particle filter: multitarget detection and tracking. In: Proceedings of European Conference on Computer Vision. Prague, Czech Republic: Springer, 2004. 28-39
[4]  Khan Z, Balch T, Dellaert F. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1805-1819
[5]  Qu W, Schonfeld D, Mohamed M. Real-time distributed multi-object tracking using multiple interactive trackers and a magnetic-inertia potential model. IEEE Transactions on Multimedia, 2007, 9(3): 511-519
[6]  Li S Z. Markov Random Field Modeling in Image Analysis. Berlin: Springer, 2009
[7]  Efron B, Tibshirani R J. An Introduction to the Bootstrap. New York: Chapman and Hall, 1993
[8]  Andrieu C, de Freitas N, Doucet A, Jordan M I. An introduction to MCMC for machine learning. Machine Learning, 2003, 50(1-2): 5-43
[9]  Isard M, Blake A. Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision, 1998, 29(1): 5-28
[10]  Shen Le-Jun, Ke Zun-Yu, Cheng Xiao-Ping. The real time tracking on athlete during the competition of ball games. China Sport Science, 2007, 27(1): 64-67(沈乐君, 柯遵渝, 程小平. 球类比赛中运动员的实时跟踪. 体育科学, 2007, 27(1): 64-67)
[11]  Gu Xin, Wang Hai-Tao, Wang Ling-Feng, Wang Ying, Chen Ru-Bing, Pan Chun-Hong. Fusing multiple features for object tracking based on uncertainty measurement. Acta Automatica Sinica, 2011, 37(5): 550-559(顾鑫, 王海涛, 汪凌峰, 王颖, 陈如冰, 潘春洪. 基于不确定性度量的多特征融合跟踪. 自动化学报, 2011, 37(5): 550-559)
[12]  Yang Tao, Li Jing, Pan Quan, Zhang Yan-Ning. A greedy searching algorithm for multiple object tracking and occlusion handling. Acta Automatica Sinica, 2010, 36(3): 375-384(杨涛, 李静, 潘泉, 张艳宁. 一种基于贪心搜索的实时多目标遮挡处理算法. 自动化学报, 2010, 36(3): 375-384)
[13]  Bar-Shalom Y, Fortmann T E. Tracking and Data Association. San Diego: Academic Press Professional, 1988
[14]  Vermaak J, Doucet A, Pérez P. Maintaining multi-modality through mixture tracking. In: Proceedings of the 9th IEEE International Conference on Computer Vision. Nice, France: IEEE, 2003. 1110-1116
[15]  Li Y, Ai H Z, Yamashita T, Lao S H, Kawade M. Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(10): 1728-1740
[16]  Breitenstein M D, Reichlin F, Leibe B, Koller-Meier E, van Gool L. Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(9): 1820-1833
[17]  Yu T, Wu Y. Collaborative tracking of multiple targets. In: Proceedings of the 2004 IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE, 2004. 834-841
[18]  Lanz O. Approximate Bayesian multibody tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1436-1449
[19]  Xue J R, Zheng N N, Zhong X P. Sequential stratified sampling belief propagation for multiple targets tracking. Science in China Series F: Information Sciences, 2006, 49(1): 48-62
[20]  Gordon N, Salmond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proceedings F: Radar and Signal Processing, 1993, 140(2): 107-113
[21]  Pitt M K, Shepard N. Filtering via simulation: auxiliary particle filters. Journal of the American Statistics Association, 1999, 94(446): 590-599
[22]  Liu J S. Monte Carlo Strategies in Scientific Computing. New York: Springer, 2001
[23]  PETS 2009 Benchmark Data [Online], available: http:// www.cvg.rdg.ac.uk/WINTERPETS09/, April 1, 2012
[24]  Yan Xiao-Xi, Han Chong-Zhao. Multiple target tracking algorithm based on online estimation of target birth intensity. Acta Automatica Sinica, 2011, 37(8): 963-972(闫小喜, 韩崇昭. 基于目标出生强度在线估计的多目标跟踪算法. 自动化学报, 2011, 37(8): 963-972)

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133