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FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking

International journal of computer vision, 2021-11, Vol.129 (11), p.3069-3087 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 ;COPYRIGHT 2021 Springer ;The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-021-01513-4

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  • Title:
    FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
  • Author: Zhang, Yifu ; Wang, Chunyu ; Wang, Xinggang ; Zeng, Wenjun ; Liu, Wenyu
  • Subjects: Accuracy ; Artificial Intelligence ; Computer Imaging ; Computer Science ; Computer vision ; Datasets ; Human-computer interaction ; Image Processing and Computer Vision ; Machine vision ; Multiple target tracking ; Object recognition ; Optimization ; Pattern Recognition ; Pattern Recognition and Graphics ; Source code ; Vision
  • Is Part Of: International journal of computer vision, 2021-11, Vol.129 (11), p.3069-3087
  • Description: Multi-object tracking (MOT) is an important problem in computer vision which has a wide range of applications. Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency. However, we find that the two tasks tend to compete with each other which need to be carefully addressed. In particular, previous works usually treat re-ID as a secondary task whose accuracy is heavily affected by the primary detection task. As a result, the network is biased to the primary detection task which is not fair to the re-ID task. To solve the problem, we present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. Note that it is not a naive combination of CenterNet and re-ID. Instead, we present a bunch of detailed designs which are critical to achieve good tracking results by thorough empirical studies. The resulting approach achieves high accuracy for both detection and tracking. The approach outperforms the state-of-the-art methods by a large margin on several public datasets. The source code and pre-trained models are released at https://github.com/ifzhang/FairMOT .
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0920-5691
    EISSN: 1573-1405
    DOI: 10.1007/s11263-021-01513-4
  • Source: ProQuest Central
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