Robust wheelchair pedestrian detection using sparse representation

Abstract

Detecting pedestrians with disability in surveillance videos is practical for the implementation of automated alert/assistance technology. This paper presents a novel approach for the dimensionality reduction which employs sparse representation to improve the generalization capability of a classifier. To characterize pedestrian with disability, we create directional maps by determining the dominant direction of motion in each local spatiotemporal region using 3D orientation filters, and then uses the maps in real-time surveillance settings to detect pre-defined types. Mathematically, the derived algorithm regards the input features as the dictionary in sparse representation, and selects the features that minimize the residual output error iteratively, thus the resulting features have a direct correspondence to the performance requirements of the given problem. Furthermore, the proposed algorithm can be regarded as a sparse classifier, which selects discriminative features and classifies the training data simultaneously. Experimental results obtained using the extensive dataset show the superior performance of our method and thus demonstrate its robustness with the novel sparse representation-based disabled pedestrian detector.