期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 31, 期 1, 页码 275-288出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2020.2975842
关键词
Target tracking; Machine learning; Recurrent neural networks; Optimization; Task analysis; Standards; Inference algorithms; Multi-object tracking; end-to-end deep learning; conditional random field; data association
资金
- National Natural Science Foundation of China [61671484, 61701548, 61906119]
- Hubei Natural Science Foundation [2018CFB503]
- Fundamental Research Funds for the Central Universities
- South-Central University for Nationalities [CZT20003]
- Shanghai Pujiang Program
This paper proposes utilizing deep conditional random field networks to address the assignment problem in multi-object tracking, optimizing unary and pairwise potentials jointly in an end-to-end learning process. Extensive experiments show that this approach outperforms existing methods on MOT datasets.
By bundling multiple complex sub-problems into a unified framework, end-to-end deep learning frameworks reduce the need for hand engineering or tuning of parameters for each component, and optimize different modules jointly to ensure the generalization of the whole deep architecture. Despite tremendous success in numerous computer vision tasks, end-to-end learnings for multi-object tracking (MOT), especially for the assignment problem in data association, have been surprisingly less investigated mainly due to the lack of available training data. Furthermore, it is challenging to discriminate target objects under mutual occlusions or to reduce identity switches in crowded scenes. To tackle these challenges, this paper proposes learning deep conditional random field (CRF) networks, aiming to model the assignment costs as unary potentials and the long-term dependencies among detection results as pairwise potentials. Specifically, we use a bidirectional long short-term memory (LSTM) network to encode the long-term dependencies. We pose the CRF inference as a recurrent neural network learning process using the standard gradient descent algorithm, where unary and pairwise potentials are jointly optimized in an end-to-end manner. Extensive experiments are conducted on the challenging MOT datasets including MOT15, MOT16 and MOT17, and the results show that the proposed algorithm performs favorably against the state-of-the-art methods.
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