Progress on Vision-Based Mobile Robot Target-Tracking Systems

DOI

10.2991/978-94-6463-821-9_53How to use a DOI?

Keywords

Mobile Robot; Target Tracking; Deep Learning; Feature Fusion

Abstract

With the rapid advancement of mobile robotics, vision-based target tracking has shown great potential in intelligent warehousing, service robotics, and autonomous driving. As a key technology for autonomous navigation and environmental interaction, target tracking plays a crucial role in enhancing the intelligence of robotic systems. However, tracking accuracy, real-time performance, and robustness remain significant challenges, especially in complex environments with occlusions, lighting variations, and dynamic targets. Traditional tracking methods often struggle with feature extraction and adaptability, limiting their effectiveness in real-world applications. Therefore, this study explores deep learning-based target tracking methods, focusing on CNNs, RNNs, Transformers, and multimodal fusion techniques. These methods enable automatic feature extraction and improve tracking performance under challenging conditions. The research findings highlight significant advancements in tracking accuracy and robustness, demonstrating superior results in mobile robotic applications. Despite improvements, challenges in real-time efficiency and adaptability remain. Future research should focus on lightweight network architectures, multimodal data integration, long-term tracking with re-identification, and algorithm optimization. This study provides a comprehensive reference for researchers and contributes to the further development and application of target tracking in mobile robotics.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hua Chen
PY  - 2025
DA  - 2025/08/31
TI  - Progress on Vision-Based Mobile Robot Target-Tracking Systems
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 522
EP  - 536
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_53
DO  - 10.2991/978-94-6463-821-9_53
ID  - Chen2025
ER  -