Fast Head Detection Algorithm via Regions of Interest

The traditional pedestrian detection systems usually scan the whole image through sliding window to find the pedestrian, this cause high computation cost. To solve this problem, this paper proposes a regions of interest based fast head detection algorithm. Motivated by the fact that the human head region usually has obvious gradient value and is not easy to be occluded, we set up the initial location model of the region of interest (ROI) by analyzing the distribution of the head gradient. After this, the K-means clustering algorithm is used to filter out the false ROIs and obtain refined candidates. Finally, the HOG-SVM framework is adopted to classify the ROIs after two times of choosing, so as to locate the human heads. Experimental results on real video sequences show that the proposed method can effectively improve the detection rate while ensuring the accuracy of detection.