The diversity and complexity of gestures can have a large impact on the reliability and accuracy of the recognition,while vision-based gesture recognition usually uses a single feature used for classification,but the feature information extracted from a single feature is limited.Therefore,the paper proposes a gesture recognition method based on the fusion of convolutional neural network(VGG16)and histogram of gradients(HOG)features,the fused features include the depth texture information of the image and the gradient direction information of the local region,and a combinatorial SVM classifier is constructed in a one-to-one manner to complete the training and testing of the gesture recognition model.The experimental results show that the recognition rate of the fused feature extraction classification reaches 97.86％under the test of the publicly available American Sign Language(ASL)dataset,which is 20.89％higher than the HOG feature classification,and 19.35％higher than the VGG16 feature extraction approach.Compared with other networks DenseNet-18,ResNet-121,the recognition rate is about 10％higher.Finally,the physical car experiment is carried out to verify the reliability and practicability of the algorithm.
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