Learning Bidirectional Temporal Cues for Video-Based Person Re-Identification

作者：Zhang, Wei; Yu, Xiaodong^*; He, Xuanyu

来源：IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(10): 2768-2776.

DOI：10.1109/TCSVT.2017.2718188

• Person re-identification
• spatio-temporal representation
• CNN
• BRNN
• surveillance

This paper presents an end-to-end learning architecture for video-based person re-identification by integrating convolutional neural networks (CNNs) and bidirectional recurrent neural networks (BRNNs). Given a video with consecutive frames, features of each frame are extracted with CNN and then are fed into the BRNN to get a final spatio-temporal representation about the video. Specifically, CNN acts as a Spatial Feature Extractor, while BRNN is expected to capture the temporal cues of sequential frames in both forward and backward directions, simultaneously. The whole network is trained end-toend with a joint identification and verification manner. Experimental results on benchmark data sets show that the proposed model can effectively learn spatio-temporal features relevant for re-identification and outperforms existing video-based person re-identification methods.

• 出版日期2018-10
• 单位山东大学