An experimental study on pedestrian classification using local features

Publication Type:
Conference Proceeding
Proceedings - IEEE International Symposium on Circuits and Systems, 2008, pp. 2741 - 2744
Issue Date:
Filename Description Size
Thumbnail2013006860OK.pdf326.48 kB
Adobe PDF
Full metadata record
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 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]. ©2008 IEEE.
Please use this identifier to cite or link to this item: