Rapid and robust human detection and tracking based on omega-shape features

Authors:
Min Li;Zhaoxiang Zhang;Kaiqi Huang;Tieniu Tan
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;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

This paper proposes a novel method for rapid and robust human detection and tracking based on the omega-shape features of people's head-shoulder parts. There are two modules in this method. In the first module, a Viola-Jones type classifier and a local HOG (Histograms of Oriented Gradients) feature based AdaBoost classifier are combined to detect headshoulders rapidly and effectively. Then, in the second module, each detected head-shoulder is tracked by a particle filter tracker using local HOG features to model target's appearance, which shows great robustness in scenarios of crowding, background distractors and partial occlusions. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.