Head-shoulder detection using joint HOG features for people counting and video surveillance in library
Liping Chen, Huibin Wu, Shuguang Zhao, Jiong Gu · 2014
Pedestrian detection is an important problem in video surveillance. While pedestrians often have diverse postures and mutual occlusion which make the detection quite difficult, their head-shoulder portions are relatively stable. Thus we choose to use head-shoulder outline features of a pedestrian for detecting. First, we apply a hierarchical classification method using Haar features and HOG features to head-shoulder location detection. Second, we define a combined feature named Joint HOG based on the symmetry of head-shoulder portion. Third, we filter out most negative samples by using the Haar classifier. Finally, we execute an elaborate HOG verification and thus obtain the head-shoulder target box expected. Experimental results show that our method achieved a real-time processing accuracy rate of nearly 90%, arguing that it is applicable to people counting and video surveillance in library.