Flow-guided feature enhancement network for video-based person re-identification

Video-based person re-identification associates sequences of the same person among surveillance camera network. Most existing works explore motion and inter-frame information on the features corrupted by spatial noises such as occlusion, blur, posture changes, etc, leading to degraded representation and matching performance. Enhancing features of each frame guarantees a more robust and discriminative final feature representation. In this paper, we propose a novel flow-guided feature enhancement network that leverages flow information to enhance low-level features. Specifically, it improves per-frame features by aggregating with the warped feature under the guidance of optical flow and the enhanced feature of previous frame in spatial attention mechanism. Then, a part-based loss is directly employed on the enhanced features to supervise the aggregation process, which can exert full capability of the network. Experiments on three widely used benchmark datasets: iLIDS-VID, PRID-2011 and MARS, demonstrate that the proposed model achieves superior performance and outperforms most of the recent state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.