A novel human detection algorithm combining HOG with LBP histogram Fourier

Abstract

Human detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detect human in video sequences has grown steadily. This paper proposed a novel approach to improve the capability of human detection. After investigating the image texture analysis methods, we adopt the new good texture descriptor, Local Binary Pattern histogram Fourier (LBPHF). Then we combine the LBPHF with the Histograms of Oriented Gradients (HOG) as feature sets, and use linear SVM to train our classifier. In the experiment on the INRIA personal dataset which is well known relatively good human detection's dataset, it is shown that our detector combining with the LBPHF significantly outperforms the other methods. Moreover, the time cost is much less and the dimension is reduced.