Abstract
Video-based group re-identification (Re-ID) remains to be a meaningful task under rare study. Group Re-ID contains the information of the relationship between pedestrians, while the video sequences provide more frames to identify the person. In this paper, we propose a spatial-temporal fusion network for the group Re-ID. The network composes of the residual learning played between the CNN and the RNN in a unified network, and the attention mechanism which makes the system focus on the discriminative features. We also propose a new group Re-ID dataset DukeGroupVid to evaluate the performance of our spatial-temporal fusion network. Comprehensive experimental results on the proposed dataset and other video-based datasets, PRID-2011, i-LIDS-VID and MARS, demonstrate the effectiveness of our model.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China (NSFC, Grant No. 61771303 and 61671289), Science and Technology Commission of Shanghai Municipality (STCSM, Grant Nos. 17DZ1205602, 18DZ1200- 102, 18DZ2270700), and SJTUYitu/Thinkforce Joint laboratory for visual computing and application. Funded by National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC).
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Xu, Q., Yang, H., Chen, L. (2019). Spatial-temporal Fusion Network with Residual Learning and Attention Mechanism: A Benchmark for Video-Based Group Re-ID. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_42
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