Pedestrian detection using principal components analysis of gradient distribution

In this paper we proposed a new method for pedestrian detection in images and videos. Our method uses a sliding window to search through images. In order to extract the features, each window is divided into overlapping cells and features are extracted from them. The feature that we extracted to describe each window is based on analysis of gradient distribution of each cell. After gradient distribution of a cell computed, the PCA is applied on it and using a mathematical expression that gauges the attitude of edges we got the feature of the cell. Putting the features of the cells next to each other forms the feature vector of the window. Then, the extracted features are classified using Support Vector Machine (SVM). Finally, the learned SVM model tested on the INRIA pedestrian dataset. The proposed method was compared with Histograms of Oriented Gradient (HOG) approach and the results show that our method has comparable detection accuracy as well as having more robustness when facing with noise.