Abstract
Human following is an essential function in many robotic systems. Most of the existing human following algorithms are based on human tracking algorithms. However, in practical scenarios, the human subject might easily disappear due to occlusions and quick movements. In order to solve the problem of occlusion, this paper proposed a classification-based human following framework. After using a pre-trained MobileNetV2 model to detect the human subjects, the robot will automatically train a classification model to identify the target person. In the end, the robot is controlled by some rule-based motion commands to follow the target human. Experimental results on several practical scenarios have demonstrated the effectiveness of the algorithm.
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Zhou, W., Dickenson, P., Cai, H., Li, B. (2022). Human Following for Mobile Robots. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13455. Springer, Cham. https://doi.org/10.1007/978-3-031-13844-7_61
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DOI: https://doi.org/10.1007/978-3-031-13844-7_61
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