Unsupervised Multi-view Pedestrian Detection

With the prosperity of the video surveillance, multiple visual sensors have been applied for an accurate localization of pedestrians in a specific area, which facilitate various applications like intelligent safety or new retailing. However, previous methods rely on the supervision from the human annotated pedestrian positions in every video frame and camera view, which is a heavy burden in addition to the necessary camera calibration and synchronization. Therefore, we propose in this paper an Unsupervised Multi-view Pedestrian Detection approach (UMPD) to eliminate the need of annotations to learn a multi-view pedestrian detector. 1) Firstly, Semantic-aware Iterative Segmentation (SIS) is proposed to extract discriminative visual representations of the input images from different camera views via an unsupervised pretrained model, then convert them into 2D segments of pedestrians, based on our proposed iterative Principal Component Analysis and the zero-shot semantic classes from the vision-language pretrained models. 2) Secondly, we propose Vertical-aware Differential Rendering (VDR) to not only learn the densities and colors of 3D voxels by the masks of SIS, images and camera poses, but also constraint the voxels to be vertical towards the ground plane, following the physical characteristics of pedestrians. 3) Thirdly, the densities of 3D voxels learned by VDR are projected onto Bird-Eyes-View as the final detection results. Extensive experiments on popular multi-view pedestrian detection benchmarks, i.e., Wildtrack and MultiviewX, show that our proposed UMPD approach, as the first unsupervised method to our best knowledge, performs competitively with the previous state-of-the-art supervised techniques. Code will be available.