SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos
This work addresses the need for standardized datasets and benchmarks in soccer video analysis, enabling automated extraction of player and team statistics, but it is incremental as it builds on existing tracking methods by providing new data.
The authors tackled the problem of limited datasets for training and benchmarking multiple object tracking in soccer videos by introducing SoccerNet-Tracking, a novel dataset with 200 sequences and a 45-minute half-time segment, fully annotated for player, referee, and ball tracking. Their analysis reveals that tracking in soccer remains unsolved, with significant challenges in fast motion and severe occlusion scenarios.
Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.