Extracting RoIs for Robust Far-infrared Pedestrian Detection on Board

Abstract

Far-infrared pedestrian detection onboard is more challenging compared to pedestrian detection of visible light. Existing works proved that the output of RoIs extraction, named as the proposal, is significantly related to recall rate and computational cost for pedestrian detection. However, it is non-trivial for RoIs extraction due to low resolution, blurred details, and pedestrian morphological features in far-infrared scenes. The paper proposes a novel RoIs extraction framework for far-infrared pedestrian detection on Board, named FIR-RoIEF, by using edge to obtain pedestrian contour feature and cascade filtering to gain valuable RoIs. Given pedestrian morphological features, we further present a vertical edge strategy to enhance pedestrian vertical features. A T-shaped template and RoIs reordering are used in the bounding box evaluation process to output pure and high-quality RoIs. Under basic and standard metrics, we perform experiments on public datasets, SCUT and KAIST, which both contain large far-infrared pedestrian objects. As for the result, given 100, 400, 1000 proposals, we both achieve the best recall in 1000 proposals, 94%, and 87% respectively. By the way, it can be proved that our method keeps a good balance between computational cost and good real-time.