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 pointspsila 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 DEFpsilas performance. Experimental results show that DEF can help HOG to decreases false alarms caused by random complicated and rigid shaped background.