An experimental evaluation of local features for pedestrian classification
Date
2007
Authors
Paisitkriangkrai, S.
Shen, C.
Zhang, J.
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Conference paper
Citation
Proceedings of the 9th International Conference on Digital Image Computing: Techniques and Applications (DICTA'07), 3-5 December, 2007: pp.53-60
Statement of Responsibility
Sakrapee Paisitkriangkrai, Chunhua Shen and Jian Zhang
Conference Name
Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (9th : 2007 : Glenelg, Australia)
Abstract
The ability to detect pedestrians is a first important step in many computer vision applications such as video surveillance. 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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© 2007 IEEE