Real-time Hand Gesture Recognition Method Based on Improved YOLOv3
摘要
The hand gesture recognition method based on artificial modeling has many problems such as low accuracy and slow speed.Therefore,this paper proposes a static hand gesture recognition method based on improved YOLOv3.By using the convolutional neural network YOLOv3 model,the commonly used RGB images are replaced by the IR,Registration of RGB,RGB and Depth images collected by Kinect equipment as dataset.The recognition results of these 4 types of images are fused to improve the recognition accuracy.The k-means clustering algorithm is used to optimize the initial candidate frame parameters in YOLOv3,so as to improve the recognition speed.On this basis,the transfer learning is used to improve the basic feature extractor to shorten the training time of the model.Experimental results show that for the recognition of static hand gestures in stream videos,the mean Average Precision(mAP) of the proposed method is 99.8% and the recognition speed is up to 52 FPS.The training time of the proposed model is 12 hours,and its recognition accuracy and speed is better than other deep learning methods such as Faster R-CNN,SSD and YOLOv2.