Abstract
The intelligent control of Automated Guided Vehicles (AGV) has essential research significance and application in logistics loading, unmanned driving, and emergency rescue. As an idealized human-computer interaction method, gesture has tremendous expressive power. Therefore, the gesture-based AGV control method is the mainstream. However, In a complex environment, noise interference can affect the precise and real-time control of AGV. To deal with this problem, a real-time AGV gesture control method based on human body part detection is proposed. We design a simple AGV control method based on human gestures by the relative relationship between human body parts in space. We extend a new branch on the Fully Convolutional One-Stage Object Detection (FCOS), which constrains the detection range of human parts. This method subtly associates the human parts with the human body, which vastly improves the anti-interference capability of gesture recognition. We train the network end-to-end on the COCO Human Parts dataset and achieve a detection accuracy of 35.4% of human parts. In addition, We collect a small dataset for validating the gesture recognition method designed in this paper and achieves an accuracy of 96.1% with a detection speed of 17.23 FPS. Our method achieves precise and convenient control of AGVs.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (62006204, 62103407), the Guangdong Basic and Applied Basic Research Foundation (2022A1515011431), and Shenzhen Science and Technology Program (RCBS20210609104516043, JSGG20210802154004014).
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Xu, Y., Gao, Q., Yu, X., Zhang, X. (2023). A Real-Time AGV Gesture Control Method Based on Body Part Detection. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14273. Springer, Singapore. https://doi.org/10.1007/978-981-99-6498-7_17
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DOI: https://doi.org/10.1007/978-981-99-6498-7_17
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