Pedestrian Pose Estimation Using Stereo Perception

This paper presents an algorithm to perform pedestrian pose estimation using a stereo vision system in the Advanced Driver Assistance Systems (ADAS) context. The proposed approach isolates the pedestrian point cloud and extracts the pedestrian pose using a visibility based pedestrian 3D model. The model accurately predicts possible self occlusions and uses them as an integrated part of the detection. The algorithm creates multiple pose hypotheses that are scored and sorted using a scheme reminiscent of the Monte Carlo techniques. The technique performs a hierarchical search of the body pose from the head position to the lower limbs. In the context of road safety, it is important that the algorithm is able to perceive the pedestrian pose as quickly as possible to potentially avoid dangerous situations, the pedestrian pose will allow to better predict the pedestrian intentions. To this end, a single pedestrian model is used to detect all pertinent poses and the algorithm is able to extract the pedestrian pose based on a single stereo depth point cloud and minimal orientation information. The algorithm was tested against data captured with an industry standard motion capture system. Accurate results were obtained, the algorithm is able to correctly estimate the pedestrian pose with acceptable accuracy. The use of stereo setup allows the algorithm to be used in many varied contexts ranging from the proposed ADAS context to surveillance or even human-computer interaction.