Pedestrian Detection and Tracking Under Dense Crowd Scene

doi:10.3969/j.issn.1006-2475.2017.01.015

Abstract

Abstract: In this paper, we present a robust and efficient method to multiple humans tracking based on head detection. Methods of extracting background are failed due to the high density population and the serious problem of shade. The head in front of the video is detected by Viola and Jones AdaBoost cascade classifier based on Haar-like features, the back of the head is detected by Logistic regression based on head profile feature. After determining the pedestrian’s head position, the appearance color histograms for the head are modeled, finally we use a particle filter algorithm to track the pedestrian’s head. The experimental results demonstrate that the proposed method is capable of tracking humans effectively in high density crowds.

Key words: pedestrian detection, head detection, people tracking, particle filter, occlusion resolving

CLC Number:

TP391

CAO Rui, WANG Min, DUAN Xiao-xiao. Pedestrian Detection and Tracking Under Dense Crowd Scene[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.01.015.

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