SoccerNet 2023 Tracking Challenge -- 3rd place MOT4MOT Team Technical Report
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Abstract (arXiv)
The SoccerNet 2023 tracking challenge requires the detection and tracking of soccer players and the ball. In this work, we present our approach to tackle these tasks separately. We employ a state-of-the-art online multi-object tracker and a contemporary object detector for player tracking. To overcome the limitations of our online approach, we incorporate a post-processing stage using interpolation and appearance-free track merging. Additionally, an appearance-based track merging technique is used to handle the termination and creation of tracks far from the image boundaries. Ball tracking is formulated as single object detection, and a fine-tuned YOLOv8l detector with proprietary filtering improves the detection precision. Our method achieves 3rd place on the SoccerNet 2023 tracking challenge with a HOTA score of 66.27.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 31 Aug 2023.
Builds on
9 of 11 references.
References · 9
- 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 910
- 2016Simple Online and Realtime Trackingcited by 868
- 2020HOTA: A Higher Order Metric for Evaluating Multi-Object Trackingcited by 552
- 2019Bag of Tricks and A Strong Baseline for Deep Person Re-identificationcited by 430
- 2022Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Trackingcited by 346
- 2019Omni-Scale Feature Learning for Person Re-Identificationcited by 295
- 2022StrongSORT: Make DeepSORT Great Againcited by 213
- 2023Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identificationcited by 132
- 2022SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videoscited by 80
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.