Name
Abstract | ||
|---|---|---|
Due to balanced accuracy and speed, one-shot models which jointly learn detection and identification embeddings, have drawn great attention in multi-object tracking (MOT). However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm. This leads to inferior performance compared with existing two-stage methods. In this paper, we first dissect the reasoning process for these two tasks, which reveals that the competition between them inevitably would destroy task-dependent representations learning. To tackle this problem, we propose a novel reciprocal network (REN) with a self-relation and cross-relation design so that to impel each branch to better learn task-dependent representations. The proposed model aims to alleviate the deleterious tasks competition, meanwhile improve the cooperation between detection and ReID. Furthermore, we introduce a scale-aware attention network (SAAN) that prevents semantic level misalignment to improve the association capability of ID embeddings. By integrating the two delicately designed networks into a one-shot online MOT system, we construct a strong MOT tracker, namely CSTrack. Our tracker achieves the state-of-the-art performance on MOT16, MOT17 and MOT20 datasets, without other bells and whistles. Moreover, CSTrack is efficient and runs at 16.4 FPS on a single modern GPU, and its lightweight version even runs at 34.6 FPS. The complete code has been released at https://github.com/JudasDie/SOTS |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIP.2022.3165376 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
Keywords | DocType | Volume |
Task analysis, Feature extraction, Target tracking, Detectors, Semantics, Object detection, Computational modeling, Multiobject tracking, reciprocal representation learning, scale-aware attention, one-shot, ID embedding | Journal | 31 |
Issue | ISSN | Citations |
1 | 1057-7149 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Liang | 1 | 0 | 0.34 |
| Zhipeng Zhang | 2 | 0 | 0.34 |
| Xue Zhou | 3 | 194 | 11.81 |
| Bing Li | 4 | 217 | 60.28 |
| Shuyuan Zhu | 5 | 156 | 24.72 |
| Weiming Hu | 6 | 5300 | 261.38 |