Abstract
Video-based person re-identification is a challenging task due to illuminations, occlusions, viewpoint changes, and pedestrian misalignment. Most previous works focus more on temporal correlation features, which leads to a lack of detailed information. In this paper, we emphasize the importance of keeping both correlation and diversity of multi-frame features simultaneously. Thus, we propose a Temporal Correlation-Diversity Representation (TCDR) network to enhance the representation of frame-level pedestrian features and the temporal feature aggregation abilities. Specifically, in order to capture correlated but diverse temporal features, we propose a Temporal-Guided Frame Feature Enhancement (TGFE) module, which explores the temporal correlation with a global perspective and enhances frame-level features to achieve the temporal diversity. Furthermore, we propose a Temporal Feature Integration (TFI) module to aggregate multi-frame features. Finally, we propose a novel progressive smooth loss to alleviate the influence of noisy frames. Extensive experiments show that our method achieves the state-of-the-art performance on MARS, DukeMTMC-VideoReID and LS-VID datasets.
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Acknowledgment
The research work is supported by the National Key Research and Development Program of China (2021AAA0140203), the Zhejiang Provincial Key Research and Development Program of China (No. 2021C01164), the Project of Chinese Academy of Sciences (E141020). Juan Cao thanks the Nanjing Government Affairs and Public Opinion Research Institute for the support of “CaoJuan Studio” and thank Chi Peng, Jingjing Jiang, Qiang Liu, and Yu Dai for their help.
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Gong, L., Zhang, R., Tang, S., Cao, J. (2022). Temporal Correlation-Diversity Representations for Video-Based Person Re-Identification. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_8
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DOI: https://doi.org/10.1007/978-3-031-18907-4_8
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