Partially Occluded Pedestrian Classification using Three Stage Cascaded Classifier

Pedestrian detection is an important area in computer vision with key applications in intelligent vehicle and surveillance systems. One of the main challenges in pedestrian detection is occlusion. In this paper, we propose a novel pedestrian detection approach capable of handling partial occlusion. Three stage cascaded classifier is used in the proposed approach. Global classifier based on HOG features and linear-SVM is first employed to classify the whole scanning window. For ambiguous patterns, a set of part-based classifiers trained on features derived from non-occluded dataset are employed on the second stage. Several fusion methods including average, maximum, linear and non-linear SVM classifiers are examined to combine the obtained part scores. The linear/non-linear fusion coefficients are estimated by learning an additional third stage SVM classifier. The training data in the third stage classifier is augmented by generating a set of artificially occluded samples which simulate real occlusion conditions commonly occurred in pedestrians. Experimental results using Daimler and INRIA data sets show the effectiveness of the proposed approach.