Semi-supervised learning of sparse representations to recognize people spatial orientation

In this paper we consider the problem of classifying people spatial orientation with respect to the camera viewpoint from 2D images. Structured multi-class feature selection allows us to control the amount of redundancy of our input data, while semi-supervised learning helps us coping with the intrinsic ambiguity of output labels. We model the multi-class classification problem with an all-pairs strategy based on the use of a coding matrix. A thorough experimental evaluation on the TUD Multiview Pedestrian benchmark dataset demonstrates the superiority of our approach w.r.t. state-of-the-art.