Abstract
This paper presents a real-time Human detection algorithm based on HOG (Histograms of Oriented Gradients) features and SVM (Support Vector Machine) architecture. Motion detection is used to extract moving regions, which can be scanned by sliding windows; detecting moving region can subtract unnecessary sliding windows of static background regions under the surveillance conditions, then detection efficiency can be improved. Every sliding window is regarded as an individual image region and HOG features are calculated as classified eigenvectors. At last, the detected video objects can be categorized into pre-defined groups of humans and other objects by using SVM classifier. Experimental results from real-time video are provided which demonstrate the effectiveness of the method.
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