Nighttime pedestrian detection using thermal imaging based on HOG feature

This research focuses on pedestrian detection using infrared thermal imager. The purpose is to locate the pedestrians from studying thermal imagery. Based on HOG (Histograms of Oriented Gradients), Adaboost algorithm is used as a way to perform the detection. The system is divided into three sections, to extract the features of the pedestrians, to train the Adaboost classifier, and to detect the pedestrian. To get the features of the pedestrians, data is gathered from inserted images. The features allow the detection to work well. The feature extraction includes image segmentation, ROI selection, and feature extraction. We have successfully located the positions of the pedestrians with the methods mentioned above. This can be applied to the development of the intelligent driver assistance system, giving more road traffic situations to the drivers throughout the night.