|Rapid and Robust Human Detection and Tracking based on Omega-Shape Features|
|Min Li; Zhaoxiang Zhang; Kaiqi Huang; Tieniu Tan
|Conference Name||16th IEEE International Conference on Image Processing
|Source Publication||IEEE International Conference on Image Processing, 2009
|Conference Date||7-10 November 2010
|Conference Place||Cairo, Egypt
|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 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.|
Image Edge Detection
|Corresponding Author||Min Li|
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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