EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View
Torben Teepe, Philipp Wolters, Johannes Gilg, Fabian Herzog, Gerhard, Rigoll

TL;DR
EarlyBird introduces an early-fusion approach in Bird's Eye View for multi-view multi-object tracking, significantly improving detection and tracking accuracy by integrating views before detection and leveraging learned re-identification features.
Contribution
The paper proposes a novel early-fusion method in BEV for multi-view tracking, enhancing performance over existing view-based approaches and demonstrating superior results on Wildtrack.
Findings
Outperforms state-of-the-art on Wildtrack with +4.6 MOTA
Achieves high accuracy in detection and tracking in BEV
Effectively learns re-identification features for temporal association
Abstract
Multi-view aggregation promises to overcome the occlusion and missed detection challenge in multi-object detection and tracking. Recent approaches in multi-view detection and 3D object detection made a huge performance leap by projecting all views to the ground plane and performing the detection in the Bird's Eye View (BEV). In this paper, we investigate if tracking in the BEV can also bring the next performance breakthrough in Multi-Target Multi-Camera (MTMC) tracking. Most current approaches in multi-view tracking perform the detection and tracking task in each view and use graph-based approaches to perform the association of the pedestrian across each view. This spatial association is already solved by detecting each pedestrian once in the BEV, leaving only the problem of temporal association. For the temporal association, we show how to learn strong Re-Identification (re-ID)…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Neural Network Applications · Fire Detection and Safety Systems
