关键词: 中国手语识别, 目标检测, 通道权重分配, 数据增强

Abstract:

The sign language recognition technology based on computer vision can bring great convenience to bilingual teaching in schools. In recent years, with the rapid development of deep learning technology, the accuracy and detection speed of sign language recognition has also been greatly improved. Different from the method of using color marking and external technology（such as Kinect’s palm positioning technology）, an improved SSD（Single-Shot Multibox Detector） network is proposed to perform target detection on gestures to complete Chinese sign language recognition. For the small target of the hand, SE-Net is embedded in the feature layer of SSD for channel weight distribution, the loss function is improved to better resolve the imbalance of positive and negative sample, and data argument is carried out by mixup. Then the recognition result will be matched with the sign language key gesture template library of China. Experiments verify that the proposed algorithm has higher recognition rate and speed in sign language recognition.

Key words: Chinese sign language recognition, object detection, channel weight distribution, data enhancement