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

• URL: http://arxiv.org/abs/2410.02638v1
• Date: Thu, 3 Oct 2024 16:23:33 GMT
• Title: Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
• Authors: Fabian Herzog, Johannes Gilg, Philipp Wolters, Torben Teepe, Gerhard Rigoll,
• Abstract summary: We introduce a graph representation that allows spatial-temporal clustering in a single, combined step. By keeping sparse appearance and positional cues of all detections in a cluster, our method can compare clusters based on the strongest available evidence. Our method does not require any training on the target scene, pre-extraction of single-camera tracks, or additional annotations.
• Score: 5.679775668038154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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