基于肤色模型的动态手势分割与识别方法研究  

Study on dynamic gesture segmentation and recognition using skin color models

作　　者：曹国华[1,2] 刘福迪 马国庆 刘丽[1,2] Cao Guohua;Liu Fudi;Ma Guoqing;Liu Li(School of Mechanical and Electrical Engineering,Changchun University of Science and Technology,Changchun 130022,China;Chongqing Research Institute,Changchun University of Science and Technology,Chongqing 401135,China)

机构地区：[1]长春理工大学机电工程学院,长春130022 [2]长春理工大学重庆研究院,重庆401135

出　　处：《电子测量技术》2024年第22期144-151,共8页Electronic Measurement Technology

基　　金：吉林省自然科学基金(YDZJ202301ZYTS423);吉林省自然科学基金(YDZJ202301ZYTS263);重庆市自然科学基金(CSTB2022NSCQ-MSX1120);重庆市自然科学基金(cstc2021jcyj-msxmX0989)项目资助。

摘　　要：为解决现有动态手势识别方法在复杂环境下因相近肤色、光照变化等因素造成势识别率不高、鲁棒性欠佳等问题,提出一种基于椭圆肤色模型的动态手势分割与识别方法。首先利用YCrCb颜色空间中的Cr分量结合OTSU阈值分割算法分割出手部区域。其次,针对手势复杂程度以及手指粗细不同的情况下直接运用形态学处理可能或导致细节丢失,影响识别的准确性问题,对传统Canny算法进行改进,并结合形态学处理对手部边缘填充;然后通过结合Kalman与改进的CamShift算法来对手势进行跟踪,完成动态手势分割;最后通过BP神经网络对分割后的动态手势进行识别,通过优化算法在GPU上的实现,利用GPU的并行处理能力加速图像处理、特征提取及神经网络前向传播等计算密集型任务。这部分优化措施显著提升了动态手势识别方法的实时性能,使其能够更好地适应于各类对实时性要求高的应用场景。实验结果表明:此方法在应对复杂背景及光照环境变化时具有较强的鲁棒性及抗干扰能力,平均识别率可达94.67%。In order to solve the problems of low potential recognition rate and poor robustness caused by similar skin tones and lighting changes in complex environments,a dynamic gesture segmentation and recognition method based on elliptical skin color model was proposed.Firstly,the Cr component in the YCrCb color space combined with the OTSU threshold segmentation algorithm was used to segment the hand region.Secondly,in view of the problem that the direct application of morphological processing may lead to the loss of details and affect the accuracy of recognition in the case of different gesture complexity and finger thickness,the traditional Canny algorithm was improved,and the morphological processing was combined with the filling of hand edges.Then,by combining Kalman and the improved CamShift algorithm to track the gestures,the dynamic gesture segmentation is completed.Finally,the segmented dynamic gestures are recognized by the BP neural network,and the implementation of the optimization algorithm on the GPU is used to accelerate the computation-intensive tasks such as image processing,feature extraction and forward propagation of the neural network by using the parallel processing capability of the GPU.This optimization measure significantly improves the real-time performance of the dynamic gesture recognition method,making it better suitable for various application scenarios with high real-time requirements.Experimental results show that the proposed method has strong robustness and anti-interference ability in response to complex background and lighting environment changes,and the average recognition rate can reach 94.67%.

关 键 词：机器视觉 动态手势分割 椭圆肤色模型 Otsu阈值分割算法 彩色-深度图像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN98[自动化与计算机技术—计算机科学与技术]