Development of Person Identification Algorithm for Mobile Robots Using Convolutional Neural Networks

Abstract

Observed color in images is easily affected by lighting conditions such as the sunlight location, weather, or time of day. Especially for image recognition processing on an outdoor navigated autonomous mobile robot, we should consider the effect of lighting conditions to achieve consistent robust person detection regardless of lighting condition change. The purpose of this study is to develop a system for detecting target persons by applying Convolutional Neural Networks. As a demonstration of person detecting capability for the proposed approach, we apply specified human finding tasks defined by Tsukuba Challenge 2021 rules. Using the proposed method, we were able to demonstrate that person can be stably detected and designated people can be judged regardless of surrounding light change.