HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

被引:512
|
作者
Luiten, Jonathon [1 ]
Osep, Aljosa [2 ]
Dendorfer, Patrick [2 ]
Torr, Philip [3 ]
Geiger, Andreas [4 ,5 ]
Leal-Taixe, Laura [2 ]
Leibe, Bastian [1 ]
机构
[1] Rhein Westfal TH Aachen, Aachen, Germany
[2] Tech Univ Munich, Munich, Germany
[3] Univ Oxford, Oxford, England
[4] Max Planck Inst Intelligent Syst, Tubingen, Germany
[5] Univ Tubingen, Tubingen, Germany
基金
英国工程与自然科学研究理事会;
关键词
Multi-object tracking; Evaluation metrics; Visual tracking; PERFORMANCE-MEASURES; MULTITARGET; ALGORITHM; FILTERS; TARGET;
D O I
10.1007/s11263-020-01375-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics overemphasize the importance of either detection or association. To address this, we present a novel MOT evaluation metric, higher order tracking accuracy (HOTA), which explicitly balances the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers. HOTA decomposes into a family of sub-metrics which are able to evaluate each of five basic error types separately, which enables clear analysis of tracking performance. We evaluate the effectiveness of HOTA on the MOTChallenge benchmark, and show that it is able to capture important aspects of MOT performance not previously taken into account by established metrics. Furthermore, we show HOTA scores better align with human visual evaluation of tracking performance.
引用
收藏
页码:548 / 578
页数:31
相关论文
共 50 条
  • [21] Multi-Object Tracking with Distributed Sensing
    Dias, Ricardo
    Lau, Nuno
    Silva, Joao
    Lim, Gi Hyun
    2016 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI), 2016, : 564 - 569
  • [22] MeMOT: Multi-Object Tracking with Memory
    Cai, Jiarui
    Xu, Mingze
    Li, Wei
    Xiong, Yuanjun
    Xia, Wei
    Tu, Zhuowen
    Soatto, Stefano
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2022, : 8080 - 8090
  • [23] A Robust Framework for Multi-object Tracking
    Jalal, Anand Singh
    Singh, Vrijendra
    ADVANCES IN COMPUTING AND COMMUNICATIONS, PT 4, 2011, 193 : 329 - 338
  • [24] HumanTop: a multi-object tracking tabletop
    Soto Candela, Emilio
    Ortega Perez, Mario
    Marin Romero, Clemente
    Perez Lopez, David C.
    Salvador Herranz, Gustavo
    Contero, Manuel
    Alcaniz Raya, Mariano
    MULTIMEDIA TOOLS AND APPLICATIONS, 2014, 70 (03) : 1837 - 1868
  • [25] SiamMOT: Siamese Multi-Object Tracking
    Shuai, Bing
    Berneshawi, Andrew
    Li, Xinyu
    Modolo, Davide
    Tighe, Joseph
    2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 12367 - 12377
  • [26] A time-weighted metric for sets of trajectories to assess multi-object tracking algorithms
    Garcia-Fernandez, Angel F.
    Rahmathullah, Abu Sajana
    Svensson, Lennart
    2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), 2021, : 363 - 370
  • [27] Multi-object tracking with robust object regression and association
    Li, Yi-Fan
    Ji, Hong-Bing
    Chen, Xi
    Lai, Yu-Kun
    Yang, Yong-Liang
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2023, 227
  • [28] INTERACTIVE MULTI-OBJECT TRACKING FOR VIRTUAL OBJECT MANIPULATION
    Guo, Yibo
    Yang, Michael Ying
    Rosenhahn, Bodo
    ISA13 - THE ISPRS WORKSHOP ON IMAGE SEQUENCE ANALYSIS 2013, 2013, II-3/W2 : 19 - 24
  • [29] Object Hypotheses as Points for Efficient Multi-Object Tracking
    Tarashima, Shuhei
    VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP, 2021, : 828 - 835
  • [30] Multi-Object Tracking Via Multi-Attention
    Wang, Xianrui
    Ling, Hefei
    Chen, Jiazhong
    Li, Ping
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,