Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.11.050 | Neurocomputing |
Keywords | Field | DocType |
Video person re-identification,Optical flow,Feature enhancement | Mars Exploration Program,Pattern recognition,Flow (psychology),Surveillance camera,Artificial intelligence,Discriminative model,Optical flow,Mathematics | Journal |
Volume | ISSN | Citations |
383 | 0925-2312 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weichao Gong | 1 | 1 | 0.35 |
| Bo Yan | 2 | 43 | 10.30 |
| Chuming Lin | 3 | 9 | 4.14 |