CASIA OpenIR  > 智能感知与计算研究中心
Rapid and Robust Human Detection and Tracking based on Omega-Shape Features
Min Li; Zhaoxiang Zhang; Kaiqi Huang; Tieniu Tan
2009-11-07
Conference Name16th IEEE International Conference on Image Processing
Source PublicationIEEE International Conference on Image Processing, 2009
Pages2545-2548
Conference Date7-10 November 2010
Conference PlaceCairo, Egypt
AbstractThis 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 head-shoulders 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.
KeywordRobustness Humans Particle Tracking Target Tracking Head Particle Filters Layout Shape Surveillance Image Edge Detection
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12706
Collection智能感知与计算研究中心
Corresponding AuthorMin Li
Recommended Citation
GB/T 7714
Min Li,Zhaoxiang Zhang,Kaiqi Huang,et al. Rapid and Robust Human Detection and Tracking based on Omega-Shape Features[C],2009:2545-2548.
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