An experimental study on pedestrian classification using local features

This paper presents an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on both the benchmarking dataset used in [1] and the NUT CBCL dataset. Both can be publicly accessed. The experimental results show that region covariance features with radial basis function (RBF) kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM reported in [1].