Age Should Not Matter: Towards More Accurate Pedestrian Detection via Self-Training

Abstract

Why is there a the disparity in the miss rates of pedestrian detection between different age attributes? In this study, we propose to (i) improve the accuracy of pedestrian detection using our pre-trained model and (ii) explore the causes of this disparity. In order to improve detection accuracy, we extend a pedestrian detection pre-training dataset, the Weakly Supervised Pedestrian Dataset (WSPD), by means of self-training, to construct our Self-Trained Person Dataset (STPD). More-over, we hypothesise the cause of the miss rate as being due to three biases: 1) the apparent bias towards “adults” versus “children,” 2) the quantity of training data bias against “chil- dren,” and 3) the scale bias of the bounding box. In addition, we constructed an evaluation dataset by manually annotat- ing “adult” and “child” bounding boxes to the INRIA Person Dataset. As a result, we confirm that the miss rate was re- duced by up to 0.4% for adults and up to 3.9% for children. In addition, we discuss the impact of the size and appearance of the bounding boxes on the disparity in miss rates and pro-vide an outlook for future research.