Pedestrian detection using HOG, LUV and optical flow as features with AdaBoost as classifier
Rauf Rabia, Ahmad Raza Shahid, Sheikh Ziauddin, Asad Safi · 2016
Pedestrian detection has been used in applications such as car safety, video surveillance, and intelligent vehicles. In this paper, we present a pedestrian detection scheme using HOG, LUV and optical flow features with AdaBoost Decision Stump classifier. Our experiments on Caltech-USA pedestrian dataset show that the proposed scheme achieves promising results of about 16.7% log-average miss rate.