An experimental evaluation of local features for pedestrian classification. In: 9th biennial conference of the Australian pattern recognition society on digital image computing techniques and applications. 3–5 December 2007, pp 53–60

The ability to detect pedestrians is a first important step in many computer vision applications such as video surveil-lance. This paper presents an experimental study on pedes-trian detection using state-of-the-art local feature extrac-tion and support vector machine (SVM) classifiers. The per-formance 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 MIT 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].