Name
Abstract | ||
|---|---|---|
We propose a novel online multiple object tracker taking structure information into account. State-of-the-art multi-object tracking (MOT) approaches commonly focus on discriminative appearance features, while neglect in different levels structure information and the core of data association. Addressing this, we design a new tracker fully exploiting structure information and encoding such information into the cost function of the graph matching model. Firstly, a new measurement is proposed to compare the structure similarity of two graphs whose nodes are equal. With this measurement, we define a complete matching which performs association in high efficiency. Secondly, for incomplete matching scenarios, a structure keeper net (SKnet) is designed to adaptively establish the graph for matching. Finally, we conduct extensive experiments on benchmarks including MOT2015 and MOT17. The results demonstrate the competitiveness and practicability of our tracker. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10489-021-02294-6 | Applied Intelligence |
Keywords | DocType | Volume |
Graph structure, Data association, Multiple object tracker | Journal | 51 |
Issue | ISSN | Citations |
11 | 0924-669X | 0 |
PageRank | References | Authors |
0.34 | 17 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Wang | 1 | 93 | 27.47 |
| Qi Zou | 2 | 43 | 13.59 |
| Qiulin Ma | 3 | 0 | 1.69 |
| Yaping Huang | 4 | 0 | 1.35 |
| Haitao Lou | 5 | 0 | 0.34 |
| Xiaoyu Wu | 6 | 0 | 0.34 |
| Huiyong Liu | 7 | 0 | 0.34 |