Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912056906612736 |
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| author | Herzog, Fabian Gilg, Johannes Wolters, Philipp Teepe, Torben Rigoll, Gerhard |
| author_facet | Herzog, Fabian Gilg, Johannes Wolters, Philipp Teepe, Torben Rigoll, Gerhard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking Herzog, Fabian Gilg, Johannes Wolters, Philipp Teepe, Torben Rigoll, Gerhard Computer Vision and Pattern Recognition 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. |
| title | Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.02638 |