Abstract
It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making it generalizable and more robust. A modified intersection over union between the current frame and the last frame is proposed as a feature to distinguish people, in addition to color, height, and location. To improve the performance of target identification in a dynamic environment, an online boosting method was adapted by continuously updating the features in every frame. Through extensive real-life experiments, the effectiveness of the proposed method was demonstrated by showing experimental results that it outperformed the previous methods.
Funder
National Research Foundation of Korea
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference56 articles.
1. ROREAS: Robot coach for walking and orientation training in clinical post-stroke rehabilitation—Prototype implementation and evaluation in field trials;Gross;Auton. Robot.,2017
2. Autonomous Vehicle Evaluation: A Comprehensive Survey on Modeling and Simulation Approaches;Alghodhaifi;IEEE Access,2021
3. Monocular person tracking and identification with on-line deep feature selection for person following robots;Koide;Robot. Auton. Syst.,2020
4. Kanchanasatian, K. A Robot Companion Algorithm for Side-by-Side Object Tracking and Following. Proceedings of the 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC).
5. Kästner, L., Fatloun, B., Shen, Z., Gawrisch, D., and Lambrecht, J. Human-following and-guiding in crowded environments using semantic deep-reinforcement-learning for mobile service robots. Proceedings of the International Conference on Robotics and Automation (ICRA).
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