Adaptive Pedestrian Detection in Infrared Images Using Background Subtraction and Local Thresholding

Infrared (IR) imaging is the order of the day with potential real life applications such as surveillance, defence, non-military applications and so on. Low contrast, poor illumination due to capturing devices and moderate to low environmental conditions are the general characterizations of IR images. In addition, the occlusion of objects make the detection more challenging. The objects considered in this paper are pedestrians. A simple and efficient single image handling pedestrian detection method is proposed in this paper. The two major tasks in the proposed method are background subtraction model and local adaptive thresholding. The major contribution of the paper is the adaptive calculation of the required parameters based on the image characteristics. Experiments are conducted on the standard OSU thermal pedestrian database to show the robustness of the proposed method. The proposed method attain detection rate of 90% under various environmental conditions which is superior than the other existing single image handling methods.