Advanced Search
Volume 41 Issue 4
Apr.  2015
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(4): 713-720.
ZHANG Cheng, MA Huadong, FU Huiyuanet al. Object tracking in surveillance videos using spatial-temporal correlation graph model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(4): 713-720. doi: 10.13700/j.bh.1001-5965.2014.0472(in Chinese)
Citation: ZHANG Cheng, MA Huadong, FU Huiyuanet al. Object tracking in surveillance videos using spatial-temporal correlation graph model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(4): 713-720. doi: 10.13700/j.bh.1001-5965.2014.0472(in Chinese)
shu

Object tracking in surveillance videos using spatial-temporal correlation graph model

doi: 10.13700/j.bh.1001-5965.2014.0472

  • Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China

  • Received Date: 28 Apr 2014
  • Rev Recd Date: 01 Aug 2014
  • Publish Date: 20 Apr 2015
    Abstract
  • Object tracking in non-overlapping multi-camera surveillance is a challenging problem since the transition time between cameras varies greatly from individual to individual with uncertainty. The key problem of object tracking in wide areas is data association and how to find correspondences between objects via camera topology. A novel graph model was proposed to capture the spatial-temporal correlation among objects, which are moving in the camera network. Source/sink regions are graph nodes, and the graph edges are constructed by the spatial and temporal constrains. Specifically, a tracking method combining appearance model and graph model was proposed to solve the problem of object re-identification and data association via bipartite matching in multi-camera object tracking. In addition, region covariance descriptor was utilized to fuse the appearance feature. Experiments with real videos validate the proposed approach.

     

    • FullText(HTML)
  • loading
    • References(20)
  • [1]
    Wang X. Intelligent multi-camera video surveillance:a review[C]//Pattern Recognition Letters.Netherlands:Academic Press,Elsevier,2013,3(4):3-19.
    [2]
    Fu H,Ma H, Ming A.EGMM:an enhanced Gaussian mixture model for detecting moving objects with intermittent stops[C]//IEEE International Conference on Multimedia and Expo.Piscataway,NJ:IEEE,2011,7041(2):1-6.
    [3]
    Fu H,Ma H, Liu L.Robust human detection with low energy consumption in visual sensor network[C]//IEEE International Conference on Mobile Ad-hoc and Sensor Networks.Piscataway,NJ:IEEE,2011:91-97.
    [4]
    Zheng W S, Gong S,Xiang T.Reidentification by relative distance comparison[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(3):653-668.
    [5]
    Wu Y,Lim J, Yang M H.Online object tracking:a benchmark[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2013,9(4):2411-2418.
    [6]
    Kalal Z, Mikolajczyk K,Matas J.Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1409-1422.
    [7]
    Makris D,Ellis T, Black J.Bridging the gaps between cameras[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2004,2:II205-II210.
    [8]
    Stauffer C. Estimating tracking sources and sinks[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshop.Piscataway,NJ:IEEE,2003:35.
    [9]
    Tieu K, Dalley G,Grimson W E L.Inference of non-overlapping camera network topology by measuring statistical dependence[C]//IEEE International Conference on Computer Vision.Piscataway ,NJ:IEEE,2005,2:1842-1849.
    [10]
    Loy C C, Xiang T,Gong S.Multi-camera activity correlation analysis [C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2009:1988-1995.
    [11]
    Wang X, Tieu K,Grimson W E L.Correspondence-free activity analysis and scene modeling in multiple camera views[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(1):56-71.
    [12]
    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[J].自动化学报,2010,36(10):1484-1489. Liu S H,Lai S M,Zhang M J.A min-cost flow based algorithm for objects association of multiple non-overlapping cameras[J].ACTA Automatica Sinica,2010,36(10):1484-1489(in Chinese).
    [13]
    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[J].自动化学报,2012,38(2):236-247. Wan J Q,Liu Q Y.Data association in visual sensor networks based on high-order spatio-temporal model[J].Acta Automatica Sinica,2012,38(2):236-247(in Chinese).
    [14]
    Song B, Roy-Chowdhury A K.Stochastic adaptive tracking in a camera network[C]//IEEE 11th International Conference on Computer Vision.Piscataway,NJ:IEEE,2007:1-8.
    [15]
    Tuzel O, Porikli F,Meer P.Region covariance:a fast descriptor for detection and classification[C]//European Conference on Computer Vision.Heidelberg:Springer,2006:589-600.
    [16]
    Gray D,Tao H. Viewpoint invariant pedestrian recognition with an ensemble of localized features[C]//European Conference on Computer Vision.Heidelberg:Springer,2008:262-275.
    [17]
    Bay H, Tuytelaars T,Van Gool L.Surf:speeded up robust features[C]//European Conference on Computer Vision.Heidelberg:Springer,2006:404-417.
    [18]
    Förstner W, Moonen B.A metric for covariance matrices[C]//Geodesy-the Challenge of the 3rd Millennium.Heidelberg:Springer,2003:299-309.
    [19]
    Kuhn H W. Variants of the Hungarian method for assignment problems[J].Naval Research Logistics Quarterly,1956,3(4):253-258.
    [20]
    Large-scale object tracking over a multiple-camera network[EB/OL] .The First IEEE International Conference on Multimedia Big Data.[2014-04-20].http://www.bigmm2015.org/Challenge.asp.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views(1253) PDF downloads(839) Cited by()
    Proportional views
    Related

    Copyright © Journal of Beijing University of Aeronautics and Astronautics

    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return