Multi-model Real-time Compressive Tracking

被引:0
|
作者
Zhang Jianming [1 ]
Jin Xiaokang
Wu Honglin
Wu You
机构
[1] Changsha Univ Sci & Technol, Hunan Prov Key Lab Intelligent Proc Big Data Tran, Changsha 410114, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Object tracking; Compressive Sensing (CS); Real-time; Multi-model; Dynamic learning rate;
D O I
10.11999/JEIT171128
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Object tracking is easily influenced by illumination, occlusion, scale, background clutter, and fast motion, and it requires higher real-time performance. The object tracking algorithm based on compressive sensing has a better real-time performance but performs weakly in tracking when object appearance is changed greatly. Based on the framework of compressive sensing, a Multi-Model real-time Compressive Tracking (MMCT) algorithm is proposed, which adopts the compressive sensing to decrease the high dimensional features for the tracking process and to satisfy the real-time performance. The MMCT algorithm selects the most suitable classifier by judging the maximum classification score difference of classifiers in the previous two frames, and enhances the accuracy of location. The MMCT algorithm also presents a new model update strategy, which employs the fixed or dynamic learning rates according to the differences of decision classifiers and improves the precision of classification. The multi-model introduced by MMCT does not increase the computational burden and shows an excellent real-time performance. The experimental results indicate that the MMCT algorithm can well adapt to illumination, occlusion, background clutter and plane-rotation.
引用
收藏
页码:2373 / 2380
页数:8
相关论文
共 16 条
  • [1] Adam A, 2006, IEEE C COMP VIS PATT, V2006, P798, DOI DOI 10.1109/CVPR.2006.256
  • [2] Robust Object Tracking with Online Multiple Instance Learning
    Babenko, Boris
    Yang, Ming-Hsuan
    Belongie, Serge
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2011, 33 (08) : 1619 - 1632
  • [3] Collins RT, 2003, PROC CVPR IEEE, P234
  • [4] ASYMPTOTICS OF GRAPHICAL PROJECTION PURSUIT
    DIACONIS, P
    FREEDMAN, D
    [J]. ANNALS OF STATISTICS, 1984, 12 (03): : 793 - 815
  • [5] Grabner H., 2006, BRIT MACH VIS C, V1, P6
  • [6] Struck: Structured Output Tracking with Kernels
    Hare, Sam
    Golodetz, Stuart
    Saffari, Amir
    Vineet, Vibhav
    Cheng, Ming-Ming
    Hicks, Stephen L.
    Torr, Philip H. S.
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (10) : 2096 - 2109
  • [7] Learning to Track at 100 FPS with Deep Regression Networks
    Held, David
    Thrun, Sebastian
    Savarese, Silvio
    [J]. COMPUTER VISION - ECCV 2016, PT I, 2016, 9905 : 749 - 765
  • [8] Tracking-Learning-Detection
    Kalal, Zdenek
    Mikolajczyk, Krystian
    Matas, Jiri
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2012, 34 (07) : 1409 - 1422
  • [9] NAM H, 2016, PROC CVPR IEEE, P4293, DOI DOI 10.1109/CVPR.2016.465
  • [10] Locally Orderless Tracking
    Oron, Shaul
    Bar-Hillel, Aharon
    Levi, Dan
    Avidan, Shai
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2015, 111 (02) : 213 - 228