Pedestrian detection using histograms of Oriented Gradients of granule feature

To robustly detect people in a video sequence is hard due to various challenges. One of the most successful discriminative features for finding people goes to the Histograms of Oriented Gradients (HOG). Although the major contour information is encoded in the HOG feature well, the background clutter disturbs the gradient information. Thus, an extension of HOG, called histograms of oriented gradient of granules (HOGG), is proposed. Instead of collecting gradient information at each pixel, the histograms of gradients in small regions are computed. HOGG with different granularity can describe the contour while ignoring the noisy edges. Moreover, the clutter background problem can be solved by encoding extra region information. With the help of the integral image technique, the evaluation of HOGG can be efficient. The final HOG+HOGG classifier obtains 92% detection rate at 10^-4 false positive per window in the experiments.