Lifting Multi-View Detection and Tracking to the Bird's Eye View

• URL: http://arxiv.org/abs/2403.12573v1
• Date: Tue, 19 Mar 2024 09:33:07 GMT
• Title: Lifting Multi-View Detection and Tracking to the Bird's Eye View
• Authors: Torben Teepe, Philipp Wolters, Johannes Gilg, Fabian Herzog, Gerhard Rigoll,
• Abstract summary: Recent advancements in multi-view detection and 3D object recognition have significantly improved performance. We compare modern lifting methods, both parameter-free and parameterized, to multi-view aggregation. We present an architecture that aggregates the features of multiple times steps to learn robust detection.
• Score: 5.679775668038154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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