Abstract Aiming at the problem of low accuracy of gesture segmentation, inadequate description of fine-grained features and poor real-time gesture recognition in complex scenes, a hand gesture recognition algorithm combined with batch renormalization and YOLOv3 is proposed.Firstly, 20 gestures are collected under complex backgrounds and different lighting conditions, the data augmentation strategies are used for sample expansion, and a standard gesture library is established.Then, the anchor is obtained through K-means dimension clustering to detect the different scale hand types; Finally, the gesture recognition model is obtained by using the transform learning and fine-tuning.In order to solve the problem that there may be large deviations between the data when YOLOv3 network are standardized in the gesture training phase and the prediction phase, the batch renormalization method is adopted to improve the accuracy of hand gesture recognition.Experiments show that the average accuracy in normal experimental environment is 97.6%.the average recognition rate of gestures is over 89.2% in the complex environment, and the recognition speed is 0.04second.
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2021, Vol. 42 
