Pedestrian Detection from Thermal Images Incorporating Saliency Features
Fatih Altay, Senem Velipasalar · 2020
Methods relying entirely on visible-range/color images start to have problems in detection tasks when there is not enough light to illuminate the scene. Thermal cameras, which operate based on the infrared radiation emitted by objects, can provide detectable information in low- or no-light conditions. In this paper, we propose a method to improve the performance of a pedestrian detection algorithm on thermal images by incorporating features from saliency maps to enrich the thermal image features. We employ a modified version of a state-of-the-art object detection network, and feed the thermal images and their saliency maps to two parallel networks. Experimental results on five different datasets show that our proposed approach performs better at detecting pedestrians in thermal images compared to its vanilla version and a baseline model.