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https://hdl.handle.net/2440/68946
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Type: | Conference paper |
Title: | An experimental study on pedestrian classification using local features |
Author: | Paisitkriangkrai, S. Shen, C. Zhang, J. |
Citation: | Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS 2008), held in Seattle, WA, 18-21 May 2008: pp.2741-2744 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2008 |
Series/Report no.: | IEEE International Symposium on Circuits and Systems |
ISBN: | 9781424416837 |
ISSN: | 0271-4310 |
Conference Name: | IEEE International Symposium on Circuits and Systems (2008 : Seattle, WA) |
Statement of Responsibility: | Sakrapee Paisitkriangkrai, Chunhua Shen and Jian Zhang |
Abstract: | 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]. |
Rights: | ©2008 IEEE |
DOI: | 10.1109/ISCAS.2008.4542024 |
Published version: | http://dx.doi.org/10.1109/iscas.2008.4542024 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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