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Rapid and Robust Human Detection and Tracking based on Omega-Shape Features
Min Li; Zhaoxiang Zhang; Kaiqi Huang; Tieniu Tan
2009-11-07
会议名称16th IEEE International Conference on Image Processing
会议录名称IEEE International Conference on Image Processing, 2009
页码2545-2548
会议日期7-10 November 2010
会议地点Cairo, Egypt
摘要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 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.
关键词Robustness Humans Particle Tracking Target Tracking Head Particle Filters Layout Shape Surveillance Image Edge Detection
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12706
专题智能感知与计算研究中心
通讯作者Min Li
推荐引用方式
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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