Abstract

We describe a novel method for human detection in single images which can detect full bodies as well as close-up views in the pres- ence of clutter and occlusion. Humans are modeled as fiexible assemblies of parts, and robust part detection is the key to the approach. The parts are represented by co-occurrences of local features which captures the spatial layout of the part’s appearance. Feature selection and the part detectors are learnt from training images using AdaBoost. The detection algorithm is very efficient as (i) all part detectors use the same initial features, (ii) a coarse-to-fine cascade approach is used for part detection, (iii) a part assembly strategy reduces the number of spurious detections and the search space. The results outperform existing human detectors.