**1. Introduction**

Recently, research has frequently explored approaches to pedestrian detection which is expected to be applied in various fields. The remarkable progress that has been made in this area is partly due to the large-scale collection of human images from the Web.

However, there are still concerns about the safety of utilizing pedestrian detection in areas such as automated driving. One of these concerns is the disparity in detection rates based on human age and race; specifically, a disparity in detection rates between "adults" and "children" has been reported when using classical human detection methods. Brando [1] affirmed that the difference in the quantity of adult versus child data in the person detection dataset is a problem that naturally arises from demographics. There are a small number of "children" in the existing pedestrian dataset, which we assume is responsible for a sample bias and a detection rate disparity between "adults" and "children."

In this paper, we constructed our Self-Trained Person Dataset (STPD) by extending the Weakly Supervised Pedestrian Dataset (WSPD) [2] to improve the accuracy of person detection. We studied the effect of each age attribute on detection performance using each pre-trained model generated by the WSPD and STPD. The INRIA Person Dataset [3] is used to evaluate the detection performance. We re-annotated both the training and test data

**Citation:** Kogure, S.; Watabe, K.; Yamada, R.; Aoki, Y.; Nakamura, A.; Kataoka, H. Age Should Not Matter: Towards More Accurate Pedestrian Detection via Self-Training. *CSFM* **2022**, *3*, 11. https://doi.org/10.3390/ cmsf2022003011

Academic Editors: Kuan-Chuan Peng and Ziyan Wu

Published: 24 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

of the INRIA Person Dataset to rigorously investigate the effect of age on the accuracy of pedestrian detection. For this re-annotation, we added the age attribute and the bounding box (bbox). In this way, we constructed a dataset for pedestrian detection validation with the age attribute. In addition, we studied the reason for the disparity in detection rate by age. Specifically, we examined the age gap in the detection rate using three experiments: (i) we clarify whether there is a difference in appearance between "adults" and "children"; (ii) we study the impact of the data augmentation of children's learning data alone on the missed rate; and (iii) finally, we compare the miss rate for each age attribute when the scale of the input image is changed. Our contributions are as follows:


### **2. Related Work**
