EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View
This work solves multi-target multi-camera tracking for applications like surveillance by providing a more accurate and efficient method, though it is incremental as it builds on existing BEV detection approaches.
The paper tackles multi-view tracking by performing detection and tracking directly in the Bird's Eye View (BEV) to address occlusion and missed detection, achieving state-of-the-art results with improvements of +4.6 MOTA and +5.6 IDF1 on Wildtrack.
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) features for each detection. The results show that early-fusion in the BEV achieves high accuracy for both detection and tracking. EarlyBird outperforms the state-of-the-art methods and improves the current state-of-the-art on Wildtrack by +4.6 MOTA and +5.6 IDF1.