Accurate pedestrian counting system based on local features

Authors:
Yu Peng;Min Xu;Zefeng Ni;Jesse S. Jin;Suhuai Luo
Affiliations:
School of DICT, University of Newcastle, NSW, Australia;Faculty of Engineering & I.T. University of Technology, Sydney, Australia;Faculty of Engineering & I.T. University of Technology, Sydney, Australia;Centre for Quantum Computation & Intelligent Systems, University of Technology, Sydney, Australia, School of Software, Tianjin University, China;School of DICT, University of Newcastle, NSW, Australia
Venue:
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Year:
2012

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Abstract

Accurate pedestrian counting are challenging in real-world due to occlusions, pedestrians' overlays or camera view sensitive. In this paper, we propose an accurate and robust pedestrian detection and counting system to address these problems. Our proposed method is group-based, where the count of people in a dense moving group is estimated as a whole. Moving groups containing single or several pedestrians are discriminated from other moving objects. Our method utilizes 9 features of each moving group within a video frame to estimate the pedestrian number in each group. Pedestrian counts are optimized by a novel tracking method, which is based on an analysis of moving groups match, split or merge. Comparison experiments with other two current methods on three benchmark surveillance videos show the effectiveness of our proposed method.