Adaptive model for robust pedestrian counting

Authors:
Jingjing Liu;Jinqiao Wang;Hanqing Lu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Venue:
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Year:
2011

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Abstract

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 advance, adaptive models are dynamically determined by the occlusion conditions of corresponding body parts. Thus, a heuristic approach with grid masks is proposed to examine visibility of certain body part. 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 promising application of the proposed approach.