Name
Abstract | ||
|---|---|---|
Multi-object tracking is a significant field in computer vision since it provides essential information for video surveillance and analysis. Several different deep learning-based approaches have been developed to improve the performance of multi-object tracking by applying the most accurate and efficient combinations of object detection models and appearance embedding extraction models. However, two-stage methods show a low inference speed since the embedding extraction can only be performed at the end of the object detection. To alleviate this problem, single-shot methods, which simultaneously perform object detection and embedding extraction, have been developed and have drastically improved the inference speed. However, there is a trade-off between accuracy and efficiency. Therefore, this study proposes an enhanced single-shot multi-object tracking system that displays improved accuracy while maintaining a high inference speed. With a strong feature extraction and fusion, the object detection of our model achieves an AP score of 69.93% on the UA-DETRAC dataset and outperforms previous state-of-the-art methods, such as FairMOT and JDE. Based on the improved object detection performance, our multi-object tracking system achieves a MOTA score of 68.5% and a PR-MOTA score of 24.5% on the same dataset, also surpassing the previous state-of-the-art trackers.</p> |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21196358 | SENSORS |
Keywords | DocType | Volume |
multi-object tracking, object detection, single-shot, traffic scenario, vehicle tracking | Journal | 21 |
Issue | ISSN | Citations |
19 | 1424-8220 | 1 |
PageRank | References | Authors |
0.43 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youngkeun Lee | 1 | 1 | 1.11 |
| Sang-ha Lee | 2 | 1 | 0.43 |
| Jisang Yoo | 3 | 9 | 3.64 |
| Soon-Chul Kwon | 4 | 9 | 2.63 |