Lifting Multi-View Detection and Tracking to the Bird's Eye View
<|reference_start|>Lifting Multi-View Detection and Tracking to the Bird's Eye View: Taking advantage of multi-view aggregation presents a promising solution to tackle challenges such as occlusion and missed detection in multi-object tracking and detection. Recent advancements in multi-view detection and 3D object recognition have significantly improved performance by strategically projecting all views onto the ground plane and conducting detection analysis from a Bird's Eye View. In this paper, we compare modern lifting methods, both parameter-free and parameterized, to multi-view aggregation. Additionally, we present an architecture that aggregates the features of multiple times steps to learn robust detection and combines appearance- and motion-based cues for tracking. Most current tracking approaches either focus on pedestrians or vehicles. In our work, we combine both branches and add new challenges to multi-view detection with cross-scene setups. Our method generalizes to three public datasets across two domains: (1) pedestrian: Wildtrack and MultiviewX, and (2) roadside perception: Synthehicle, achieving state-of-the-art performance in detection and tracking. https://github.com/tteepe/TrackTacular<|reference_end|>
@article{teepe2024lifting,
    title={Lifting Multi-View Detection and Tracking to the Bird's Eye View},
    author={Torben Teepe, Philipp Wolters, Johannes Gilg, Fabian Herzog, Gerhard
  Rigoll},
    journal={arXiv preprint arXiv:2403.12573},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12573},
    primaryClass={cs.CV}
}