Name
Abstract | ||
|---|---|---|
Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle tracking framework guided by the crossroad zones. The framework includes: (1) Use mature detection and vehicle re-identification models to extract targets and appearance features. (2) Use modified JDE-Tracker (without detection module) to track single-camera vehicles and generate single-camera tracklets. (3) According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering in Adjacent Cameras for multi-camera tracklets matching. Through the above techniques, our method obtained an IDF1 score of 0.8095, ranking first on the leaderboard (1). The code will be released later. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPRW53098.2021.00466 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGITION WORKSHOPS (CVPRW 2021) |
DocType | ISSN | Citations |
Conference | 2160-7508 | 0 |
PageRank | References | Authors |
0.34 | 10 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chong Liu | 1 | 0 | 1.01 |
| Yuqi Zhang | 2 | 4 | 7.17 |
| Hao Luo | 3 | 123 | 10.02 |
| Jiasheng Tang | 4 | 1 | 0.69 |
| Weihua Chen | 5 | 0 | 1.01 |
| Xianzhe Xu | 6 | 0 | 1.35 |
| fan wang | 7 | 15 | 16.24 |
| Hao Li | 8 | 17 | 4.37 |
| Yi-Dong Shen | 9 | 727 | 56.57 |