Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion
This paper presents an approach to improving visual object tracking performance by dynamically fusing the results of two trackers, where the scheduling of trackers is determined by a support vector machine (SVM). By classifying the outputs of other trackers, our method learns their behaviors and exp...
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MDPI AG
2024-11-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/12/12/239 |
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| author | Vincenzo M. Scarrica Antonino Staiano |
| author_facet | Vincenzo M. Scarrica Antonino Staiano |
| author_sort | Vincenzo M. Scarrica |
| collection | DOAJ |
| description | This paper presents an approach to improving visual object tracking performance by dynamically fusing the results of two trackers, where the scheduling of trackers is determined by a support vector machine (SVM). By classifying the outputs of other trackers, our method learns their behaviors and exploits their complementarity to enhance tracking accuracy and robustness. Our approach consistently surpasses the performance of individual trackers within the ensemble. Despite being trained on only 4 sequences and tested on 144 sequences from the VOTS2023 benchmark, our approach achieves a Q metric of 0.65. Additionally, our fusion strategy demonstrates versatility across different datasets, achieving 73.7 MOTA on MOT17 public detections and 82.8 MOTA on MOT17 private detections. On the MOT20 dataset, it achieves 68.6 MOTA on public detections and 79.7 MOTA on private detections, setting new benchmarks in multi-object tracking. These results highlight the potential of using an ensemble of trackers with a learner-based scheduler to significantly improve tracking performance. |
| format | Article |
| id | doaj-art-ed2dead1ad6d4afb8e7e160e827f8b72 |
| institution | DOAJ |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-ed2dead1ad6d4afb8e7e160e827f8b722025-08-20T02:50:43ZengMDPI AGTechnologies2227-70802024-11-01121223910.3390/technologies12120239Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker FusionVincenzo M. Scarrica0Antonino Staiano1National PhD Program in AI—Agrifood and Environment, University of Naples Federico II, Corso Umberto I 40, 80138 Naples, ItalyDepartment of Science and Technology, University of Naples Parthenope, Centro Direrzione di Napoli, 80143 Naples, ItalyThis paper presents an approach to improving visual object tracking performance by dynamically fusing the results of two trackers, where the scheduling of trackers is determined by a support vector machine (SVM). By classifying the outputs of other trackers, our method learns their behaviors and exploits their complementarity to enhance tracking accuracy and robustness. Our approach consistently surpasses the performance of individual trackers within the ensemble. Despite being trained on only 4 sequences and tested on 144 sequences from the VOTS2023 benchmark, our approach achieves a Q metric of 0.65. Additionally, our fusion strategy demonstrates versatility across different datasets, achieving 73.7 MOTA on MOT17 public detections and 82.8 MOTA on MOT17 private detections. On the MOT20 dataset, it achieves 68.6 MOTA on public detections and 79.7 MOTA on private detections, setting new benchmarks in multi-object tracking. These results highlight the potential of using an ensemble of trackers with a learner-based scheduler to significantly improve tracking performance.https://www.mdpi.com/2227-7080/12/12/239multi-object trackingvisual object tracking segmentationhuman-crowd trackingsupport vector machine |
| spellingShingle | Vincenzo M. Scarrica Antonino Staiano Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion Technologies multi-object tracking visual object tracking segmentation human-crowd tracking support vector machine |
| title | Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion |
| title_full | Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion |
| title_fullStr | Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion |
| title_full_unstemmed | Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion |
| title_short | Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion |
| title_sort | learning from outputs improving multi object tracking performance by tracker fusion |
| topic | multi-object tracking visual object tracking segmentation human-crowd tracking support vector machine |
| url | https://www.mdpi.com/2227-7080/12/12/239 |
| work_keys_str_mv | AT vincenzomscarrica learningfromoutputsimprovingmultiobjecttrackingperformancebytrackerfusion AT antoninostaiano learningfromoutputsimprovingmultiobjecttrackingperformancebytrackerfusion |