영상내의신체 핵심 좌표 데이터를 활용한 머신러닝기반수어 인식연구.

This paper proposes a sign language recognition system based on machine learning, which can infer the meaning of body movement in realtime video. It has a feature that motion vectors constructed with keypoint data of moving body are used for machine learning. This enables the proposed system to reduce the training time of an artificial neural network model and to enhance the recognition accuracy. Since those motion vectors have the same size and format regardless of the different expression times of words, the confidence level of an inference could be further increased. To correctly divide a part of video corresponding to each word in a sentence, we propose an iterative evaluation method of confidence levels for inferences with the gradual length adjustment of video fragment. The experimental results showed that our system outperforms a conventional method using the video itself for training and inference, in training time, inference speed, confidence level of inference and accuracy of video fragmentation. [ABSTRACT FROM AUTHOR]