Occlusion Detection and Recovery in Video Object Tracking Based on Adaptive Particle Filters

被引:0
|
作者
Duan, Zhuohua [1 ]
Cai, Zixing [2 ]
Yu, Jinxia [3 ]
机构
[1] Shaoguan Univ, Sch Comp Sci, Shaoguan 512005, Peoples R China
[2] Cent South Univ Changsha, Sch Informat Sci & Engn, Changsha 410083, Peoples R China
[3] Henan Politech Univ, Dept Comp Sci & Technol, Jiaozuo 454003, Peoples R China
关键词
Object tracking; Particle Filter; Occlusion detection; Occlusion Recovery; Adaptive;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Occlusion detection and recovery is a challenging task in robust real-time tracking of non-rigid objects. Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. The paper presents a method for occlusion detection and recovery for object tracking with adaptive particle filter. Firstly, object occlusion is detected with normalization factor. Secondly, adaptive transition function is employed to recovery from occlusion. Lastly, particle number is changed according to occlusion state. Experimental results show the presented method can detect occlusion and recover from it quickly.
引用
收藏
页码:466 / +
页数:2
相关论文
共 50 条
  • [31] An Algorithm of Adaptive Scale Object Tracking in Occlusion
    Zhao, Congmei
    MATERIALS SCIENCE, ENERGY TECHNOLOGY, AND POWER ENGINEERING I, 2017, 1839
  • [32] Object tracking using color-based Kalman particle filters
    Xia, LM
    2004 7TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS 1-3, 2004, : 679 - 682
  • [33] Multi-Feature Based Multiple Particle Filters for Object Tracking
    Ma, Ying-dong
    Liu, Yu-chen
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE: TECHNIQUES AND APPLICATIONS, AITA 2016, 2016, : 307 - 314
  • [34] Augmented particle samples based optimal convolutional filters for object tracking
    An, Xiaowei
    Liang, Quanquan
    Sun, Nongliang
    MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (03) : 4473 - 4491
  • [35] MEASUREMENT-BASED RECLUSTERING FOR MULTIPLE OBJECT TRACKING WITH PARTICLE FILTERS
    Nieto, Marcos
    Cuevas, Carlos
    Salgado, Luis
    2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, 2009, : 4097 - 4100
  • [36] Augmented particle samples based optimal convolutional filters for object tracking
    Xiaowei An
    Quanquan Liang
    Nongliang Sun
    Multimedia Tools and Applications, 2021, 80 : 4473 - 4491
  • [37] Development of Object-Tracking and Classification Algorithm with Variable Detection Areas based on Kalman and Particle Filters
    Lee, Hye Won
    Min, Hee Sun
    Park, Seong Hyun
    Park, Jeong Hyun
    Jang, Mun Jung
    Oh, Kwang Seok
    TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS A, 2021, 45 (09) : 789 - 804
  • [38] Temporal validation of Particle Filters for video tracking
    SanMiguel, Juan C.
    Cavallaro, Andrea
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2015, 131 : 42 - 55
  • [39] Object Detection and Tracking Under Occlusion for Object-Level RGB-D Video Segmentation
    Xie, Qian
    Remil, Oussama
    Guo, Yanwen
    Wang, Meng
    Wei, Mingqiang
    Wang, Jun
    IEEE TRANSACTIONS ON MULTIMEDIA, 2018, 20 (03) : 580 - 592
  • [40] Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences
    Li, Hui
    Liu, Yun
    Wang, Chaunxu
    Zhang, Shujun
    Cui, Xuehong
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2016, 2016