Abstract To solve the problem of low performance in person re-identification caused by large distribution differences between the training and testing sets in corruption scenarios, high background complexity and excessive noise types, a domain adaptive person re-identification model based on camera perception is proposed. The model aligns the image distribution of different cameras during the training phase by introducing and fully utilizing camera information. During the testing phase, temporal information is employed for ranking optimization, reducing the impact of distribution differences between the training and testing sets. The issues of background complexity and noise types are effectively addressed. The model not only effectively mitigates the impact of damaged images from the perspective of dataset processing but also significantly improves the performance of the model in corruption scenarios through quadratic weighting of sorting optimization. Experiments on Market-1501, DukeMTMC-reID and CUHK03 datasets demonstrate the effectiveness of the proposed algorithm.
Fund:National Natural Science Foundation of China(No.62172229), Natural Science Foundation of Jiangsu Province(No.BK20221349)
Corresponding Authors:
HUANG Pu, Ph.D., associate professor. His research interests include pattern recognition and machine lear-ning.
About author:: YANG Zhangjing, Ph.D., associate professor. His research interests include compu-ter vision and pattern recognition. WU Shuli, Master student. His research interests include pattern recognition and machine learning. YANG Guowei, Ph.D., professor. His research interests include machine intelligence theory and methods,intelligent systems and pattern recognition.
YANG Zhangjing,WU Shuli,HUANG Pu等. Domain Adaptive Person Re-identification Model Based on Camera Perception[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(5): 383-397.
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