Monocular Perception of Biological Motion - Clutter and Partial Occlusion

Abstract

The problem of detecting and labeling a moving human body viewed monocularly in a cluttered scene is considered. The task is to decide whether or not one or more people are in the scene (detection), to count them, and to label their visible body parts (labeling). It is assumed that a motion-tracking front end is supplied: a number of moving features, some belonging to the body and some to the background are tracked for two frames and their position and velocity is supplied (Johansson display). It is not guaranteed that all the body parts are visible, nor that the only motion present is the one of the body. The algorithm is based on our previous work [12]; we learn a probabilistic model of the position and motion of body features, and calculate maximum-likelihood labels efficiently using dynamic programming on a triangulated approximation of the probabilistic model. We extend those results by allowing an arbitrary number of body parts to be undetected (e.g. because of occlusion) and by allowing an arbitrary number of noise features to be present. We train and test on walking and dancing sequences for a total of approximately 10^4 frames. The algorithm is demonstrated to be accurate and efficient.