Directional entropy feature for human detection

In this paper we propose a novel feature, called directional entropy feature (DEF), to improve the performance of human detection under complicated background in images. DEF describe the regularity of region by computing the entropy value of edge points’ spatial distribution in specific direction, so DEF has the discriminating power for regular and random pattern. We combine Histogram of Oriented Gradient (HOG) feature with DEF to construct a human detection classifier to test DEF’s performance. Experimental results show that DEF can help HOG to decreases false alarms caused by random complicated and rigid shaped background.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Long Meng, Liang Li, Shuqi Mei, Weiguo Wu
• Keywords: Computer Vision, Directional Entropy Feature, Human Detection, ICPR 2008, Test Def Performance