  With the prosperity of the video surveillance, multiple cameras have been
applied to accurately locate pedestrians in a specific area. However, previous
methods rely on the human-labeled annotations in every video frame and camera
view, leading to heavier burden than 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 via 2D-3D mapping. 1) Firstly,
Semantic-aware Iterative Segmentation (SIS) is proposed to extract unsupervised
representations of multi-view images, which are converted into 2D pedestrian
masks as pseudo labels, via our proposed iterative PCA and zero-shot semantic
classes from vision-language models. 2) Secondly, we propose Geometry-aware
Volume-based Detector (GVD) to end-to-end encode multi-view 2D images into a 3D
volume to predict voxel-wise density and color via 2D-to-3D geometric
projection, trained by 3D-to-2D rendering losses with SIS pseudo labels. 3)
Thirdly, for better detection results, i.e., the 3D density projected on
Birds-Eye-View from GVD, we propose Vertical-aware BEV Regularization (VBR) to
constraint them to be vertical like the natural pedestrian poses. Extensive
experiments on popular multi-view pedestrian detection benchmarks Wildtrack,
Terrace, and MultiviewX, show that our proposed UMPD approach, as the first
fully-unsupervised method to our best knowledge, performs competitively to the
previous state-of-the-art supervised techniques. Code will be available.
