Feasibility Study on Deep Learning Scheme for Sign Language Motion Recognition
| Autor: | Tatsunori Ozawa, Hiromitsu Nishimura, Hiroshi Tanaka, Eiji Ota, Kazuki Sakamoto |
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| Rok vydání: | 2018 |
| Předmět: |
Scheme (programming language)
Artificial neural network Computer science business.industry Deep learning 05 social sciences Feature extraction Frame (networking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) 010501 environmental sciences Sign language 01 natural sciences Motion (physics) 0502 economics and business Computer vision Artificial intelligence 050207 economics business computer 0105 earth and related environmental sciences computer.programming_language |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319936581 CISIS |
| DOI: | 10.1007/978-3-319-93659-8_103 |
| Popis: | This paper presents the results of a feasibility study of a deep learning scheme for sign language motion recognition. Capturing the motions used in sign language was conducted using specially designed colored gloves and an optical camera. Deep learning and conventional classification schemes were used for motion recognition, and their results are compared. In a deep learning process each frame of motion data is passed directly to AlexNet for feature extraction. Although the structure of the neural network and optional parameters for deep learning have not been optimized at this stage, it was verified that the accuracy of recognition ranged from 59.6% to 72.3% for twenty-five motions. Though this performance is inferior to that of conventional schemes, it is considered that these results indicate the feasibility of using a deep learning scheme for sign language motion recognition. |
| Databáze: | OpenAIRE |
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