| Autor: |
Young-Min Song, Kwangjin Yoon, Young-Chul Yoon, Kin Choong Yow, Moongu Jeon |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 7, Pp 165103-165121 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2953276 |
| Popis: |
In this paper, we propose an efficient online multi-object tracking method based on the Gaussian mixture probability hypothesis density (GMPHD) filter and occlusion group management scheme where a hierarchical data association is utilized for the GMPHD filter to reduce the false negatives caused by missed detection. The hierarchical data association consisting of two modules, detection-to-track and track-to-track associations, can recover the lost tracks and their switched IDs. In addition, the proposed grouping management scheme handles occlusion problems with two main parts. The first part, “track merging” can merge the false positive tracks caused by false positive detections from occlusions. The occlusion of the false positive tracks is usually measured with some metric. In this research, we define the occlusion measure between visual objects, as sum-of-intersection-over-each-area (SIOA) instead of the commonly used intersection-over-union (IOU). The second part, “occlusion group energy minimization (OGEM)” prevents the occluded true positive tracks from false “track merging”. Each group of the occluded objects is expressed with an energy function and an optimal hypothesis will be obtained by minimizing the energy. We evaluate the proposed tracker in benchmarks such as MOT15 and MOT17 which are public datasets for multi-person tracking. An ablation study in training dataset reveals not only that “track merging” and “OGEM” complement each other, but also that the proposed tracking method shows more robust performance and less sensitiveness than baseline methods. Also, the tracking performance with SIOA is better than that with IOU for various sizes of false positives. Experimental results show that the proposed tracker efficiently handles occlusion situations and achieves competitive performance compared to the state-of-the-art methods. In fact, our method shows the best multi-object tracking accuracy among the online and real-time executable methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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