Adaptive Model for Robust Pedestrian Counting

Toward robust pedestrian counting with partly occlusion, we put forward a novel model-based approach for pedestrian detection. Our approach consists of two stages: pre-detection and verification. Firstly, based on a whole pedestrian model built up in advanced, adaptive models are dynamic determined by the occlusion condition of corresponding body parts. Thus, a heuristic approach with grid masks is proposed to examine visibility of certain body parts. Using part models for template matching, we adopt an approximate branch structure for preliminary detection. Secondly, Bayesian framework is utilized to verify and optimize the pre-detection results. Reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is used to solve such problem of high dimensions. Experiments and comparison demonstrate the promising of the proposed approach.