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

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 efficie...

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Vydáno v:	International journal of computer vision Ročník 129; číslo 11; s. 3069 - 3087
Hlavní autoři:	Zhang, Yifu, Wang, Chunyu, Wang, Xinggang, Zeng, Wenjun, Liu, Wenyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.11.2021 Springer Springer Nature B.V
Témata:	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 FairMOT Multi-object tracking One-shot Anchor-free Real-time inference
ISSN:	0920-5691, 1573-1405
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	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 .
AbstractList	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 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. 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 .
Audience	Academic
Author	Zeng, Wenjun Wang, Xinggang Zhang, Yifu Wang, Chunyu Liu, Wenyu
Author_xml	– sequence: 1 givenname: Yifu surname: Zhang fullname: Zhang, Yifu organization: Huazhong University of Science and Technology – sequence: 2 givenname: Chunyu surname: Wang fullname: Wang, Chunyu organization: Microsoft Research Asia – sequence: 3 givenname: Xinggang orcidid: 0000-0001-6732-7823 surname: Wang fullname: Wang, Xinggang email: xgwang@hust.edu.cn organization: Huazhong University of Science and Technology – sequence: 4 givenname: Wenjun surname: Zeng fullname: Zeng, Wenjun organization: Microsoft Research Asia – sequence: 5 givenname: Wenyu surname: Liu fullname: Liu, Wenyu organization: Huazhong University of Science and Technology
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Snippet	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...
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SubjectTerms	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
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Title	FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
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