基于视觉的神经网络三维动态手势识别方法综述

Review of Vision-based Neural Network 3D Dynamic Gesture Recognition Methods

动态手势识别作为一种重要的人机交互手段而受到广泛关注,其中基于视觉的识别方式因其使用便利性和低成本的优势成为新一代人机交互的首选技术。以人工神经网络为中心,综述了基于视觉的手势识别方法研究进展,分析了不同类型人工神经网络在手势识别中的发展现状,调研并归纳总结了待识别数据和训练数据集的类型及特点;此外,通过开展性能对比实验,客观评估了不同类型的人工神经网络,并对结果进行了分析。最后,对调研内容进行了总结,对该领域面临的挑战和存在的问题进行了阐述,对动态手势识别技术的发展趋势进行了展望。

Dynamic gesture recognition,as an important means of human-computer interaction,has received widespread attention.Among them,the visual-based recognition method has become the preferred choice for the new generation of human-computer interaction due to its convenience and low cost.Centered on artificial neural networks,this paper reviews the research progress of visual-based gesture recognition methods,analyzes the development status of different types of artificial neural networks in gesture recognition,investigates and summarizes the types and characteristics of data to be recognized and training datasets.In addition,through performance comparison experiments,different types of artificial neural networks are objectively evaluated,and the results are analyzed.Finally,based on the summary of the research content,the challenges and problems faced in this field are elaborated,and the development trend of dynamic gesture recognition technology is prospected.