Study and Analysis of Pedestrian Detection in Thermal Images Using YOLO and SVM

Pedestrian detection and driver assistance applications draws an important role on safety measure such as speed control, crash control, sensing crash and occupant location detection. The pedestrian accidents occurs due to the vulnerable traffic users such as humans, stranded or moving vehicle or other obstacles. To avoid the pedestrian accident, this paper proposes a model that can accomplish pedestrian detection automatically using Histogram of Gradient (HOG) and You Only Look Once (YOLO) algorithm. The experiments are carried out on Forward Looking Infrared Radar (FLIR) starter thermal dataset consisting of 5000 images. The HOG algorithm is implemented on these thermal image samples and is classified using Support Vector Machine (SVM) classifier. The accuracy of YOLO is calculated using intersection over union method between the ground truth and the predicted bounding box. To further improve the safety of the user an alarm is designed to alert the user on sight of pedestrians during night.