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12 November 2024 Research and application of real-time human posture recognition technology based on TensorFlow.js and CNN model
Xiya Yu, Yao Tan, Shuxian Gao, Yuhan Zhang, Haiying Fang
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 1339527 (2024) https://doi.org/10.1117/12.3049898
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
With the continuous progress of society and the rapid development of technology, emerging technologies such as artificial intelligence are becoming increasingly prevalent in daily life. In particular, in the field of human pose recognition, traditional solutions have mostly relied on high-performance servers or specialized hardware, which have limitations such as high cost, poor real-time performance, and low convenience. Therefore, developing a low-cost, high-performance human pose recognition system is of great significance for promoting technological progress and meeting people's needs for intelligent living. TensorFlow.js is an open-source machine learning library that makes it possible for AI models to run in Web browser environments. On the basis of TensorFlow.js technology, combined with the front-endVue.js framework, an online real-time human pose recognition system was developed on the Web browser end. By calling the PoseNet model and utilizing the CNN model to optimize the overall learning performance, the COCO key points are defined and recognized, and the pose recognition model is deployed on the Web browser end, lowering the system's usage threshold and improving user access efficiency. At the same time, it ensures a relatively high recognition accuracy, reducing dependence on server resources, and achieving lightweight, low-cost real-time pose recognition detection and analysis function.
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Xiya Yu, Yao Tan, Shuxian Gao, Yuhan Zhang, and Haiying Fang "Research and application of real-time human posture recognition technology based on TensorFlow.js and CNN model", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 1339527 (12 November 2024); https://doi.org/10.1117/12.3049898
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KEYWORDS
Data modeling
Image processing
Machine learning
Video
Video processing
Data processing
Education and training
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