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https://hdl.handle.net/2440/68945
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Type: | Conference paper |
Title: | An experimental evaluation of local features for pedestrian classification |
Author: | Paisitkriangkrai, S. Shen, C. Zhang, J. |
Citation: | Proceedings of the 9th International Conference on Digital Image Computing: Techniques and Applications (DICTA'07), 3-5 December, 2007: pp.53-60 |
Publisher: | IEEE Computer Society |
Publisher Place: | Online |
Issue Date: | 2007 |
ISBN: | 0769530672 9780769530673 |
Conference Name: | Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (9th : 2007 : Glenelg, Australia) |
Statement of Responsibility: | Sakrapee Paisitkriangkrai, Chunhua Shen and Jian Zhang |
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]. |
Rights: | © 2007 IEEE |
DOI: | 10.1109/DICTA.2007.4426775 |
Published version: | http://dx.doi.org/10.1109/dicta.2007.4426775 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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