Center-Surround Contrast Features for Pedestrian Detection
Inspired by the human vision system, in this paper we propose a specifically organized kind of center-surround contrast features and show their suitability for pedestrian detection. These contrasts are computed from a novel combination of both local color and gradient statistics aggregated quickly for arbitrary sized square cells. We exploit our contrast features in a rich multi-scale and -direction fashion between each central cell and its neighbors and boost the significant ones for pedestrian detection. Experimental results on the INRIA and Caltech pedestrian datasets show that our method achieves state-of-the-art performance.