Robust Pedestrian Detection Using Low Level and High Level Features

In this paper, we introduce pedestrian detection using combination of low level features like CNN, HOG and Haar with high level features. Two kinds of high level features were used in this paper. One is related to the probability of existence of human’s face, which obtained from combination of skin colorand possible location and area for the human’s face. The other is related to the probability of existence of human’s anti-bodywhich obtained by curvature checking of vertical edges, situation of them relative to each other and location of them in the detection window. Several different structures were studied and their results were compared on a diagram. Also the average execution times of them were gathered in a table. Atfirst, we show that appending the high level features to every low level feature improves the performance of detection very much and then, with proper arrangement of several features, it is possible to improve the performance of detection further without increasing the execution time. For evaluation of the proposed algorithm, INRIA database and a video sequence were used.

Keywords:

convolutional neural network , pedestrian detection , face detection , anti-body detection , HOG