Pedestrian Detection in Video Images via Error Correcting Output Code Classification of Manifold Subclasses

Pedestrian detection in images and video frames is challenged by the view and posture problem. In this paper, we propose a new pedestrian detection approach by error correcting output code (ECOC) classification of manifold subclasses. The motivation is that pedestrians across views and postures form a manifold and that the ECOC method constructs a nonlinear classification boundary that can discriminate the manifold from negative samples. The pedestrian manifold is first constructed with a local linear embedding algorithm and then divided into subclasses with a K-means clustering algorithm. The neighboring relationships of these subclasses are used to make the encoding rule for ECOCs, which we use to train multiple base classifiers with histogram of oriented gradient features and linear support vector machines. In the detection procedure, image windows are tested with all base classifiers, and their output codes are fed into an ECOC decoding procedure to decide whether it is a pedestrian or not. Experiments on three data sets show that the results of our approach improve the state of the art.