基于SVM与Inception-v3的手势识别

针对传统机器视觉的手势识别方法识别准确率低，抗干扰能力差等问题，提出了一种基于支持向量机（Support Vector Machine，SVM）手势分割和迁移学习的静态手势识别方法.本文使用SVM和迁移学习方法相结合构建新的手势识别模型，利用SVM对样本进行手势分割，将Inception-v3模型作为卷积神经网络模型基础，对网络参数进行fine-tuning，将预先经过手势分割处理后的样本导入模型训练，调整超参数得到新的最优手势识别模型，并在一定干扰环境下测试，得到测试结果.测试结果表明该方法识别准确率和实时反馈效率均高于传统方法，能高效识别手势，满足实际应用需求.

Gesture Recognition Based on SVM and Inception-v3

Aiming at the problems of low recognition accuracy and poor anti-interference ability of traditional machine vision gesture recognition methods, a static gesture recognition method based on Support Vector Machine (SVM) gesture segmentation and transfer learning is proposed. This study uses SVM and transfer learning method to build a new gesture recognition model, uses SVM to segment the sample gesture, uses the Inception-v3 model as the basis of Convolutional Neural Network (CNN) model, carries out fine tuning on the network parameters, imports the sample processed by gesture segmentation into the model training, adjusts the super parameters using fine-tuning to get the new optimal gesture recognition model. The test results, obtained in disturbed environment, show that the recognition accuracy and real-time feedback efficiency of this method are higher than those of traditional methods, which can effectively recognize gesture and meet the practical application requirements.