Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
<|reference_start|>Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking: Accurate online multiple-camera vehicle tracking is essential for intelligent transportation systems, autonomous driving, and smart city applications. Like single-camera multiple-object tracking, it is commonly formulated as a graph problem of tracking-by-detection. Within this framework, existing online methods usually consist of two-stage procedures that cluster temporally first, then spatially, or vice versa. This is computationally expensive and prone to error accumulation. We introduce a graph representation that allows spatial-temporal clustering in a single, combined step: New detections are spatially and temporally connected with existing clusters. By keeping sparse appearance and positional cues of all detections in a cluster, our method can compare clusters based on the strongest available evidence. The final tracks are obtained online using a simple multicut assignment procedure. Our method does not require any training on the target scene, pre-extraction of single-camera tracks, or additional annotations. Notably, we outperform the online state-of-the-art on the CityFlow dataset in terms of IDF1 by more than 14%, and on the Synthehicle dataset by more than 25%, respectively. The code is publicly available.<|reference_end|>
@article{herzog2024spatial-temporal,
    title={Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking},
    author={Fabian Herzog, Johannes Gilg, Philipp Wolters, Torben Teepe, and
  Gerhard Rigoll},
    journal={arXiv preprint arXiv:2410.02638},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02638},
    primaryClass={cs.CV}
}