Multiview Detection with Cardboard Human Modeling

Multiview detection uses multiple calibrated cameras with overlapping fields of view to locate occluded pedestrians. In this field, existing methods typically adopt a “human modeling - aggregation” strategy. To find robust pedestrian representations, some intuitively incorporate 2D perception results from each frame, while others use entire frame features projected to the ground plane. However, the former does not consider the human appearance and leads to many ambiguities, and the latter suffers from projection errors due to the lack of accurate height of the human torso and head. In this paper, we propose a new pedestrian representation scheme based on human point cloud modeling. Specifically, using ray tracing for holistic human depth estimation, we model pedestrians as upright, thin cardboard point clouds on the ground. Then, we aggregate the point clouds of the pedestrian cardboard across multiple views for a final decision. Compared with existing representations, the proposed method explicitly leverages human appearance and reduces projection errors significantly by relatively accurate height estimation. On four standard evaluation benchmarks, our method achieves very competitive results. The code and data are available at https://github.com/Jiahao-Ma/MvCHM.