BEVTrack: Multi-View Multi-Human Registration and Tracking in the Bird’s Eye View

We handle a new problem of multi-view multi-human tracking in the bird's eye view (BEV). Different from previous works, we require neither the calibration among the multi-view cameras nor the actually captured BEV video. This makes the studied problem closer to real-world applications, however, more challenging. For this purpose, in this work, we propose a novel BEVTrack scheme. Specifically, given multi-view videos, we first use a virtual BEV transform module to obtain the BEV for each view. Then, we propose a unified BEV alignment module to fuse the respectively generated BEVs, in which we specifically design the self-supervised losses by considering both the spatial consistency and the temporal continuity. During the inference, we design the camera-subject collaborative registration and tracking strategy to make use of the mutual dependence between the multi-view cameras and the multiple targets, to achieve the desired BEV tracking. We also build a new benchmark for training and evaluation, the experimental results on which have verified the rationality of the problem and the effectiveness of our method.