DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Park, Kyoungsoo | - |
dc.contributor.advisor | 박경수 | - |
dc.contributor.author | Park, Geonha | - |
dc.date.accessioned | 2021-05-13T19:33:16Z | - |
dc.date.available | 2021-05-13T19:33:16Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=911328&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/284719 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2020.2,[iii, 27 p. :] | - |
dc.description.abstract | Hearing impaired people use sign language to communicate with other people. Unfortunately, average people do not know about sign language, so hearing impaired people are experiencing a difficulty in government offices or banks because they are hard to communicate with officers. To address this problem, cloud services or studies for interpreting sign language through artificial intelligence are emerging recently . However, among them, the Korean sign language interpretation model places substantial computation burden on the cloud server due to heavy deep learning model and unnecessary computations. In this paper, we propose a two-stage inference system that greatly reduces the burden on cloud servers by effective task division. In particular, we focus on the front-end kiosk, which only serves to store and send videos in the existing Interpreting system. After deploying a deep learning model that is trained to detect sign language in the front-end kiosk, by sending only sign language videos instead of sending videos always to the cloud server, we can significantly reduce the workloads of the cloud server. In addition, through optimizations of the system, the amount of computation in the sign language detection model can be reduced by more than six times. In this way, we minimize additional delays in the kiosk by adapting the sign language detection model to the constraints of the kiosk's computational resources. Finally, by using frame interpolation and call sign method, and improving sign language detection process, we can achieve a better system accuracy in corner cases as well as in general cases. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Deep learning▼aSign language detection▼aCloud service▼aTwo-stage inference system▼aReal time | - |
dc.subject | 딥러닝▼a수화 감지▼a클라우드 서비스▼a두 단계 추론 시스템▼a실시간 | - |
dc.title | Efficient two-stage inference for sign language in real-time | - |
dc.title.alternative | 효율적인 실시간 수어 감지를 위한 two-stage inference 시스템 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 박건하 | - |
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