A cascade SVM approach for head-shoulder detection using histograms of oriented gradients

Abstract

This paper presents a head-shoulder detection approach using cascade SVM and histograms of oriented gradients (HOG). The HOG features which are extracted from variable-size blocks can capture salient features of head-shoulder automatically. A two stage cascade using SVM approach is designed to be the classifier. During detection, the majority of negative windows are rejected at the first stage, leaving a relatively small number of windows to be classified at the second stage, which improves the speed and precision of the detector. Due to the large number of possible target locations in an image, we applied camera self-calibration approach to facilitate the estimation for the size and location of the detection window. The experiments on surveillance videos from Trecvid 2008 proved that our approach can achieve fast and accurate head-shoulder detection.