Simultaneous Detection of Pedestrians, Pose, and the Camera Viewpoint from 3D Models

This paper describes a pedestrian detection trained from the projected suggestive contours of the 3D models and an estimation of its 3D pose instead of using multiple 2D training images. The first part explains the 3D mesh model training for pedestrian detection; the suggestive contours projected to various viewpoints enables to avoid hand-crafted training of 2D images. The second part depicts extracting the features and measuring the similarity in the space of diffusion tensor fields. By measuring the similarity and ordering the trained 3D models, the 3D camera viewpoint and pose of the detected pedestrians can also be estimated. Experiments show the effectiveness of our method.