Multiview Pedestrian Detection Based on Vector Boosting

Abstract. In this paper, a multiview pedestrian detection method based on Vector Boosting algorithm is presented. The Extended Histograms of Oriented Gradients (EHOG) features are formed via dominant orientations in which gradient orientations are quantified into several angle scales that divide gradient orientation space into a number of dominant orientations. Blocks of combined rectangles with their dominant orientations constitute the feature pool. The Vector Boosting algorithm is used to learn a tree-structure detector for multiview pedestrian detection based on EHOG features. Further a detector pyramid framework over several pedestrian scales is proposed for better performance. Experimental results are reported to show its high performance.

• Type: Conference
• Year: 2007
• Where: ACCV
• Authors: Cong Hou, Haizhou Ai, Shihong Lao
• Keywords: ACCV 2007, Computer Vision, Dominant Orientations, Multiview Pedestrian Detection, Vector Boosting Algorithm