dc.citation.conferencePlace |
KO |
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dc.citation.conferencePlace |
제주 |
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dc.citation.endPage |
832 |
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dc.citation.startPage |
829 |
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dc.citation.title |
2022년 대한전자공학회 하계종합학술대회 |
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dc.contributor.author |
김승빈 |
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dc.contributor.author |
이규호 |
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dc.date.accessioned |
2024-01-31T20:09:21Z |
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dc.date.available |
2024-01-31T20:09:21Z |
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dc.date.created |
2022-08-04 |
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dc.date.issued |
2022-07-01 |
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dc.description.abstract |
Despite its high accuracy, 3D-CNN, which is neural network for action recognition, have not been able to be utilized in mobile processor due to massive computation and memory footprint of 3D convolution. To facilitate real-time operation of 3D-CNN for hand gesture recognition, this paper proposes two key features: 1) Inter-frame Differential Aware(IDA) input method to maximally utilize the activation sparsity; 2) IDA Network(IDANet) optimized for IDA input method for reducing network parameter. IDANet reduced 96% of parameters and achieved 83.2 activation sparsity throughout the network and 79.97% accuracy on NvGesture data set. As a result, IDANet suggest possibility of real-time operation of 3D-CNN for hand gesture recognition in a mobile platform. |
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dc.identifier.bibliographicCitation |
2022년 대한전자공학회 하계종합학술대회, pp.829 - 832 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/75746 |
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dc.language |
한국어 |
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dc.publisher |
대한전자공학회 |
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dc.title |
모바일 환경에서의 실시간 손동작 인식을 위한 경량화된 3D-CNN |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2022-06-29 |
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