Name
Abstract | ||
|---|---|---|
Although refinement is commonly used in visual tasks to improve pre-obtained results, it has not been studied for Multiple Object Tracking (MOT) tasks. This could be attributed to two reasons: i) it has not been explored what kinds of errors should — and could — be reduced in MOT refinement; ii) the refinement target, namely, the tracklets, are intertwined and interactive in a 3D spatio-temporal space, and therefore changing one tracklet may affect the others. To tackle these issues, i) we define two types of errors in imperfect tracklets, as Mix-up Error and Cut-off Error, to clarify the refinement goal; ii) we propose a Refining MOT Framework (ReMOT), which first splits imperfect tracklets and then merges the split tracklets with appearance features improved by self-supervised learning. Experiments demonstrate that ReMOT can make significant improvements to state-of-the-art MOT results as powerful post-processing. As a new application, we demonstrate that ReMOT has the potential of being used to assist semi-automatic MOT data annotation and partially release humans from the tedious work. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.imavis.2020.104091 | Image and Vision Computing |
Keywords | DocType | Volume |
Multiple object tracking,refinement | Journal | 106 |
ISSN | Citations | PageRank |
0262-8856 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Yang | 1 | 1 | 0.35 |
| Xin Chang | 2 | 1 | 0.35 |
| Sakriani Sakti | 3 | 257 | 65.02 |
| Yang Wu | 4 | 84 | 18.42 |
| Satoshi Nakamura | 5 | 4 | 4.84 |