Pedestrian detection by modeling local convex shape features

This paper presents a pedestrian model built collectively on a group of strong local convex shape descriptors. The pedestrian model captures the most important features of a pedestrian: head, body contour, arms, legs and crotch, and is robust to variances in appearances and partial occlusions. For an image set of 2571 pedestrians and 4369 car and background images, the pedestrian recognition system, which was built upon the proposed pedestrian model, gave a recognition rate of 98.8% with a false positive rate of 1.56%. Furthermore, the pedestrian recognition requires a very small set of prototypes of pedestrians and non-pedestrians.