Pedestrian Detection using KPCA and FLD Algorithms

摘要：

A pedestrian detection method by using kernel principle component analysis (KPCA) and Fisher linear discriminant (FLD) is presented in this paper. The basic idea of this method is to first utilize the KPCA algorithm to perform feature extraction, which obtains the nonlinear principle components in the high dimension feature space composed of haar wavelet coefficients, and then implement classification via the FLD algorithm in the KPCA-transformed space. The twophase classification approach is also regarded as the essence of kernel fisher discriminant (KFD) in other works. The effectiveness of the proposed method for detecting people is verified using the DaimlerChrysler pedestrian classification benchmark dataset.

关键词： Pedestrian detection kernel principle component analysis fisher linear discriminant feature extraction kernel function

作者: Ying-hong Liang Zhi-yan Wang Sen Guo Xiao-wei Xu Xiao-ye Cao

作者单位: School of Computer Science, South China University of Technology, Guangzhou 510640, China;Shenzhen I School of Computer Science, South China University of Technology, Guangzhou 510640, China Shenzhen Institute of Information Technology, Shenzhen 518029, China

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）