An experimental study on pedestrian classification using local features
Date
2008
Authors
Paisitkriangkrai, S.
Shen, C.
Zhang, J.
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Conference paper
Citation
Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS 2008), held in Seattle, WA, 18-21 May 2008: pp.2741-2744
Statement of Responsibility
Sakrapee Paisitkriangkrai, Chunhua Shen and Jian Zhang
Conference Name
IEEE International Symposium on Circuits and Systems (2008 : Seattle, WA)
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].
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Dissertation Note
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©2008 IEEE