Human Body Detection using Histogram of Oriented Gradients and SVM

Human detection is a challenging task in many fields because it is difficult to detect humans due to their variable appearance and posture. Detecting humans accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. This paper consists of efficient human detection in static images using Histogram of Oriented Gradients (HOG) for local feature extraction and support vector machine (SVM) classifiers. Histogram of oriented gradient (HOG) gives an accurate description of the contour of human body. Based on HOG and support vector machine (SVM) theory, a classifier for human is obtained. I have to evaluate the performance of pedestrian histogram of oriented gradients (HOG) and Support Vector Machine on INRIA human database images.