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arXiv · 2410.02638

Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking

Fabian Herzog·Johannes Gilg·Philipp Wolters·Torben Teepe·Gerhard Rigoll
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Abstract

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.

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Fabian Herzog, Johannes Gilg, Philipp Wolters, Torben Teepe, Gerhard Rigoll. 2024-10-03. Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking. https://arxiv.org/abs/2410.02638

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