Who is partner: A new perspective on data association of multi-object tracking

被引:2
|
作者
Ding, Yuqing [1 ]
Sun, Yanpeng [1 ]
Li, Zechao [1 ]
机构
[1] Nanjing Univ Sci & Technol, Nanjing 210014, Peoples R China
基金
中国国家自然科学基金;
关键词
Data association; Multi -object tracking; Kalman filter; PERFORMANCE;
D O I
10.1016/j.imavis.2023.104737
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Occlusion problem refers to the challenge of accurately tracking blocked or occluded objects. It occurs when mul-tiple objects are moving nearby or overlapping with each other in the scene. Despite its frequent occurrence in multi-object tracking (MOT) tasks, occlusion is often overlooked by researchers. This paper proposes a simple and effective method to partially solve the occlusion problems of multi-object tracking by developing the Partner Mining Module (PMM) and the Partner Updating Module (PUM). The PMM module mines the space relationship between objects, and the PUM module uses the relationship obtained by the PMM module to update lost tracklets' positions. Importantly, these two modules can be integrated into existing data association-based multi-object tracking methods without any additional training expenses. Furthermore, this study proposes novel methods for computing measurement uncertainty to enhance trajectory accuracy. Experiments conducted on MOT16 and MOT17 datasets show the effectiveness of the proposed modules. Integration of PMM and PUM into original methods substantially enhances the IDF1 score by 1 point.& COPY; 2023 Elsevier B.V. All rights reserved.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] Referring Multi-Object Tracking
    Wu, Dongming
    Han, Wencheng
    Wang, Tiancai
    Dong, Xingping
    Zhang, Xiangyu
    Shen, Jianbing
    2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2023, : 14633 - 14642
  • [32] Multi-Object Tracking in the Dark
    Wang, Xinzhe
    Ma, Kang
    Liu, Qiankun
    Zou, Yunhao
    Fu, Ying
    2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2024, 2024, : 382 - 392
  • [33] Multi-object Tracking with Spatial-Temporal Tracklet Association
    You, Sisi
    Yao, Hantao
    Bao, Bing-Kun
    Xu, Changsheng
    ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2024, 20 (05)
  • [34] Enhancing the association in multi-object tracking via neighbor graph
    Liang, Tianyi
    Lan, Long
    Zhang, Xiang
    Peng, Xindong
    Luo, Zhigang
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2021, 36 (11) : 6713 - 6730
  • [35] On the detection-to-track association for online multi-object tracking
    Lin, Xufeng
    Li, Chang-Tsun
    Sanchez, Victor
    Maple, Carsten
    PATTERN RECOGNITION LETTERS, 2021, 146 : 200 - 207
  • [36] Multi-object trajectory tracking
    Mei Han
    Wei Xu
    Hai Tao
    Yihong Gong
    Machine Vision and Applications, 2007, 18 : 221 - 232
  • [37] A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos
    Chen, Ting
    Pennisi, Andrea
    Li, Zhi
    Zhang, Yanning
    Sahli, Hichem
    REMOTE SENSING, 2018, 10 (09)
  • [38] Multi-Object Tracking Based on Key Point Detection and Association
    Liu, Yibo
    Xi, Zhenghao
    Computer Engineering and Applications, 2023, 59 (13) : 156 - 163
  • [39] Online Multi-Object Tracking based on Hierarchical Association Framework
    Ju, Jaeyong
    Kim, Daehun
    Ku, Bonhwa
    Ko, Hanseok
    Han, David K.
    PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), 2016, : 1273 - 1281
  • [40] Pedestrian multi-object tracking algorithm based on improved YOLOX and multi-level data association
    Han K.
    Peng J.
    Journal of Railway Science and Engineering, 2024, 21 (01) : 94 - 105