Research Article
Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence
@INPROCEEDINGS{10.1007/978-3-031-28990-3_15, author={Lu Changkai and Liu Yiwen and Gao Yanxia and Shi Yuanquan and Peng Xiaoning}, title={Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence}, proceedings={Edge Computing and IoT: Systems, Management and Security. Third EAI International Conference, ICECI 2022, Virtual Event, December 13-14, 2022, Proceedings}, proceedings_a={ICECI}, year={2023}, month={3}, keywords={Edge Intelligence Gesture Recognition Neural Networks YOLOv5}, doi={10.1007/978-3-031-28990-3_15} }- Lu Changkai
Liu Yiwen
Gao Yanxia
Shi Yuanquan
Peng Xiaoning
Year: 2023
Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence
ICECI
Springer
DOI: 10.1007/978-3-031-28990-3_15
Abstract
In recent years, gestures have been widely used in many fields. For example, human-computer interaction, virtual reality, gesture translation, etc. The reason for its rapid development is due to the emergence of deep learning and artificial intelligence under today’s society. Due to dynamic gestural interactions, such large intelligent models are often characterized by many parameters, large sample size, frequent parameter updates, and high communication volume. Based on this feature, we propose a cloud-based edge design architecture approach for gesture recognition based on an improved YOLOv5 network model optimization by changing different gestures, background interference, which uses a 21-layer model for the neural network. With the use of edge intelligence, the computational accuracy can be improved by 10.6% over the traditional YOLOv5 with a MAP value of 93.3%. The final recall rate is also improved by 3.6%. The parameter model is only 43.6% of the original one. This shows the practicality and operability of using edge computing as a technique in gesture recognition, as well as the small improvement cost and obvious effect.
