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基于高阶时空模型的视觉传感网络数据关联方法

万九卿 刘青云

万九卿, 刘青云. 基于高阶时空模型的视觉传感网络数据关联方法. 自动化学报, 2012, 38(2): 236-247. doi: 10.3724/SP.J.1004.2012.00236
引用本文: 万九卿, 刘青云. 基于高阶时空模型的视觉传感网络数据关联方法. 自动化学报, 2012, 38(2): 236-247. doi: 10.3724/SP.J.1004.2012.00236
WAN Jiu-Qing, LIU Qing-Yun. Data Association in Visual Sensor Networks Based on High-order Spatio-temporal Model. ACTA AUTOMATICA SINICA, 2012, 38(2): 236-247. doi: 10.3724/SP.J.1004.2012.00236
Citation: WAN Jiu-Qing, LIU Qing-Yun. Data Association in Visual Sensor Networks Based on High-order Spatio-temporal Model. ACTA AUTOMATICA SINICA, 2012, 38(2): 236-247. doi: 10.3724/SP.J.1004.2012.00236

基于高阶时空模型的视觉传感网络数据关联方法

doi: 10.3724/SP.J.1004.2012.00236
详细信息
    通讯作者:

    万九卿, 北京航空航天大学自动化学院讲师. 主要研究领域方向为信号/图像/视频处理, 统计推理与机器学习, 目标检测、跟踪与识别, 复杂系统故障诊断与健康管理. E-mail: wanjiuqing@gmail.com

Data Association in Visual Sensor Networks Based on High-order Spatio-temporal Model

  • 摘要: 数据关联是视觉传感网络联合监控系统的基本问题之一. 本文针对存在漏检条件下视觉传感网络的数据关联问题, 提出高阶时空观测模型并在此基础上建立了数据关联问题的动态贝叶斯网络描述. 给出了数据关联精确推理算法并分析了其计算复杂性, 接着根据不同的独立性假设提出两种近似推理算法以降低算法运算量, 并将提出的推理算法嵌入到EM算法框架中,使该算法能够应用于目标外观模型未知的情况. 仿真和实验结果表明了所提方法的有效性.
  • [1] Gilbert A, Bowden R. Tracking objects across cameras by incrementally learning inter-camera color calibration and patterns of activity. In: Proceedings of the 9th European Conference on Computer Vision. Graz, Austria: Springer, 2006. 125-136[2] Javed O, Shafique K, Rasheed Z, Shah M. Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views. Computer Vision and Image Understanding, 2008, 109(2): 146-162[3] Song B, Roy-Chowdhury A K. Robust tracking in a camera network: a multi-objective optimization framework. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(4): 582-596[4] Liu Shao-Hua, Lai Shi-Ming, Zhang Mao-Jun. A min-cost flow based algorithm for objects association of multiple non-overlapping cameras. Acta Automatica Sinica, 2010, 36(10): 1484-1489(刘少华, 赖世铭, 张茂军. 基于最小费用流模型的无重叠视域多摄像机目标关联算法. 自动化学报, 2010, 36(10): 1484-1489)[5] Zajdel W, Klose B. A sequential Bayesian algorithm for surveillance with nonoverlapping cameras. International Journal of Pattern Recognition and Artificial Intelligence, 2005, 19(8): 977-996[6] Camp F, Bernardin K, Stiefelhagen R. Person tracking in camera networks using graph-based Bayesian inference. In: Proceedings of the 3rd ACM/IEEE International Conference on Distributed Smart Cameras. Como, Italy: IEEE, 2009. 1-8[7] Kim H, Romberg J, Wolf W. Multi-camera tracking on a graph using Markov chain Monte Carlo. In: Proceedings of the 3rd ACM/IEEE International Conference on Distributed Smart Cameras. Como, Italy: IEEE, 2009. 1-8[8] 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[9] Goyat Y, Chateau T, Bardet F. Vehicle trajectory estimation using spatio-temporal MCMC. EURASIP Journal on Advances in Signal Processing, 2010: Article ID 712854, 8 pages[10] Zajdel W, Klose B. Gaussian mixture models for multi-sensor tracking. In: Proceedings of the 15th Dutch-Belgian Artificial Intelligence Conference. Nijmegen, Netherlands: BNAIC, 2003. 371-378[11] Zajdel W. Bayesian Visual Surveillance: from Object Detection to Distributed Cameras [Ph.D. dissertation], University of Amsterdam, Netherlands, 2006[12] Murphy K P. Dynamic Bayesian Networks: Representation, Inference and Learning [Ph.D. dissertation], University of California, Berkeley, USA, 2002[13] Boyen X, Koller D. Tractable inference for complex stochastic processes. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Madison, USA: Morgan Kaufmann, 1998. 33-42[14] Shachter R D. Bayes-ball: the rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams). In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Madison, USA: Morgan Kaufmann, 1998. 480-487[15] Dempster A P, Laird N M, Rubin D B. Maximum-likelihood from incomplete data via the EM algorithm. Journal of Royal Statistics Society, Series B, 1977, 39(1): 1-38[16] Bilmes J A. A Gentle Tutorial on the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models, Technical Report TR-97-021, University of California, Berkeley, USA, 1998[17] Jepson A D, Fleet D J, El-maraghi T F. Robust online appearance models for visual tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, USA: IEEE, 2001. 415-422[18] Wan J, Liu Q. Efficient data association in visual sensor networks with missing detection. EURASIP Journal on Advances in Signal Processing, 2011, 2011: Article ID 176026, 25 pages
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出版历程
  • 收稿日期:  2010-11-24
  • 修回日期:  2011-05-15
  • 刊出日期:  2012-02-20

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