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