Multiple object tracking: A literature review

被引:400
|
作者
Luo, Wenhan [1 ,2 ]
Xing, Junliang [3 ,6 ]
Milan, Anton [4 ]
Zhang, Xiaoqin [5 ]
Liu, Wei [1 ]
Kim, Tae-Kyun [2 ]
机构
[1] Tencent AI Lab, Shenzhen, Peoples R China
[2] Imperial Coll London, London, England
[3] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
[4] Amazon Res & Dev Ctr, Berlin, Germany
[5] Wenzhou Univ, Wenzhou, Peoples R China
[6] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
关键词
Multi-object tracking; Data association; Survey; MULTITARGET TRACKING; PERFORMANCE EVALUATION; MULTIOBJECT TRACKING; VISUAL SURVEILLANCE; MODEL; MOTION; ASSOCIATION; PROPAGATION; VEHICLE; SET;
D O I
10.1016/j.artint.2020.103448
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multiple Object Tracking (MOT) has gained increasing attention due to its academic and commercial potential. Although different approaches have been proposed to tackle this problem, it still remains challenging due to factors like abrupt appearance changes and severe object occlusions. In this work, we contribute the first comprehensive and most recent review on this problem. We inspect the recent advances in various aspects and propose some interesting directions for future research. To the best of our knowledge, there has not been any extensive review on this topic in the community. We endeavor to provide a thorough review on the development of this problem in recent decades. The main contributions of this review are fourfold: 1) Key aspects in an MOT system, including formulation, categorization, key principles, evaluation of MOT are discussed; 2) Instead of enumerating individual works, we discuss existing approaches according to various aspects, in each of which methods are divided into different groups and each group is discussed in detail for the principles, advances and drawbacks; 3) We examine experiments of existing publications and summarize results on popular datasets to provide quantitative and comprehensive comparisons. By analyzing the results from different perspectives, we have verified some basic agreements in the field; and 4) We provide a discussion about issues of MOT research, as well as some interesting directions which will become potential research effort in the future. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:23
相关论文
共 50 条
  • [21] Studying visual attention using the multiple object tracking paradigm: A tutorial review
    Meyerhoff, Hauke S.
    Papenmeier, Frank
    Huff, Markus
    ATTENTION PERCEPTION & PSYCHOPHYSICS, 2017, 79 (05) : 1255 - 1274
  • [22] Attentional capture in multiple object tracking
    Pichlmeier, Sebastian
    Pfeiffer, Till
    JOURNAL OF VISION, 2021, 21 (08): : 1 - 20
  • [23] Contextual cueing in multiple object tracking
    Ogawa, Hirokazu
    Watanabe, Katsumi
    Yagi, Akihiro
    VISUAL COGNITION, 2009, 17 (08) : 1244 - 1258
  • [24] A Survey on Multiple Object Tracking Algorithm
    Fan, Litong
    Wang, Zhongli
    Cai, Baigen
    Tao, Chuanqi
    Zhang, Zhiyi
    Wang, Yinling
    Li, Shanwen
    Huang, Fengtian
    Fu, Shuangfu
    Zhang, Feng
    2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), 2016, : 1855 - 1862
  • [25] Multiple Object Tracking in Noise Background
    Dechterenko, Filip
    Lukavsky, Jiri
    PERCEPTION, 2019, 48 : 107 - 107
  • [26] Crowding limits multiple object tracking
    Sayim, B.
    de-Wit, L.
    Wagemans, J.
    PERCEPTION, 2014, 43 (01) : 7 - 7
  • [27] Multiple Object Tracking for Occluded Particles
    Qian, Yifei
    Ji, Ru
    Duan, Yuping
    Yang, Runhuai
    IEEE ACCESS, 2021, 9 : 1524 - 1532
  • [28] Multiple object tracking with correlation learning
    Wang, Qiang
    Zheng, Yun
    Pan, Pan
    Xu, Yinghui
    arXiv, 2021,
  • [29] Graph Networks for Multiple Object Tracking
    Li, Jiahe
    Gao, Xu
    Jiang, Tingting
    2020 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2020, : 708 - 717
  • [30] Spatial Reference in Multiple Object Tracking
    Jahn, Georg
    Papenmeier, Frank
    Meyerhoff, Hauke S.
    Huff, Markus
    EXPERIMENTAL PSYCHOLOGY, 2012, 59 (03) : 163 - 173