Combining Structure and Appearance Cues for Real-Time Pedestrian Detection

Abstract

We present a real-time pedestrian detection system which uses cues derived from structure and appearance classification. We discuss several novel ideas to achieve computational efficiently while improving on both detection and false-alarm rates: (i) At the front end of our system we employ stereo to detect pedestrians in 3D range maps, and to classify surrounding structure such as buildings, trees, poles etc. in the scene. The structure classification efficiently labels substantial amount of non-relevant image regions and guides the further computationally expensive process to focus on relatively small image parts; (ii) We improve the appearance-based classifier based on HoG descriptors by performing template matching with 2D human shape contour fragments that results in improved localization and accuracy; (iii) We train individual classifier at several depth ranges that allow us to account for appearance and 2D shape changes at variable distances in front of the camera. Our method is evaluated on publicly available datasets and is shown to match or exceed the performance of leading pedestrian detectors in terms of accuracy as well as achieving real-time computation (10 Hz), which makes it adequate for deployment in field robots and other navigation platforms.