Abstract
The detection of pedestrian which has been widely used in digital surveillance systems is a popular topic in computer vision. This paper mainly discusses a system of pedestrian detection in video sequences captured from a stationary camera hanging in a public scene. We describe an efficient system combining background subtraction based on Gaussian Mixture Model (GMM) and object classification based on Histograms of Oriented Gradients (HOG). We first process moving objects segmentation using GMM. Then a HOG detector is used to classify the moving objects into person and none-person. Experimental results on video sequences have demonstrated that the real-time tracking system can process 15 to 30 frames per second robustly with a high accuracy.
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