Partially occluded pedestrian classification using histogram of oriented gradients and local weighted linear kernel support vector machine
One of the main challenges in pedestrian classification is partial occlusion. This study presents a new method for pedestrian classification with partial occlusion handling. The proposed method involves a set of part-based classifiers trained on histogram of oriented gradients features derived from non-occluded pedestrian data set. The score of each part classifier is then employed to weight features used to train a second stage full-body classifier. The full-body classifier based on local weighted linear kernel support vector machine is trained using both non-occluded and artificially generated partial occlusion pedestrian dataset. The new kernel allows to significantly focus on the non-occluded parts and reduce the impact of the occluded ones. Experimental results on real-world dataset, with both partially occluded and non-occluded data, show high performance of the proposed method compared with other state-of-the-art methods.