A pedestrian detection method based on SVM classifier and optimized Histograms of Oriented Gradients feature

Author

Zhang, Guangyuan ; Gao, Fei ; Liu, Cong ; Liu, Wei ; Yuan, Huai

Author_Institution

Sch. of Inf. Eng., Shenyang Univ., Shenyang, China

Volume

6

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

3257

Lastpage

3260

Abstract

Pedestrian detection by camera sensor is an important function in intelligent vehicle. Histograms of Oriented Gradients (HOG) features is a kind of efficient pedestrian feature. We optimized the HOG features to achieve an accurate human detection system. We don´t normalize the input detection windows but resize the cell and block by same ratio. In the processing of calculate the HOG features, the Integral image is used for a better performance. The linear SVM is used as a classifier for the pedestrian detection result. Simulation results showed that the approach method is effective.

Keywords

gradient methods; image sensors; object detection; pattern classification; support vector machines; SVM classifier; camera sensor; histograms of oriented gradients feature; human detection system; pedestrian detection method; support vector machine; Feature extraction; Histograms; Humans; Pixel; Support vector machine classification; Training; HOG; Integral Image; Pedestrian Detection; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5582537

Filename

5582537