Vision based pedestrian detection using histogram of oriented gradients, adaboost, linear support vector machines and optical flow

Pedestrian detection systems are valuable in a variety of applications including advanced driver assistance systems and advanced robots. This study aims to develop a vision-based pedestrian detection system for moving platforms. It uses Histogram of Oriented Gradients (HOG) as feature descriptor, AdaBoost and Linear Support Vector Machines (SVM) as a classifiers and Optical Flow for discerning the pedestrians direction. The entire system is tested and evaluated in both publicly available databases and personally acquired videos. The pedestrian detection system has been tested and results show that it can detect pedestrians. Experiments showed that the system is up 20% faster than default detector