S3D: Scalable Pedestrian Detection via Score Scale Surface Discrimination

Abstract

Pedestrian detection has remained an important research topic in both the computer vision and multimedia communities because of its importance in practical applications, such as driving assistance and video surveillance. Existing methods compare the response score with a fixed threshold to determine whether a candidate region contains pedestrians and produce dissatisfactory results that contain either missed detections or false detections, which are difficult to balance. This situation has a serious impact under the condition of variable scale. This paper investigates the functional relationship between the scores and scales of pedestrians. By designing experiments with multiple scales, we have found a discriminant surface in the score scale space. Pedestrians can be distinguished at various scale levels according to their locations on the discriminant surface. The proposed approach is evaluated using four challenging pedestrian detection datasets, including Caltech, INRIA, ETH, and KITTI, and the superior experimental results are achieved when compared with baseline methods.