심층 신경망의 최적화를 통한 소규모 행동 분류 문제의 행동 인식 방법 = A Method of Activity Recognition in Small-Scale Activity Classification Problems via Optimization of Deep Neural Networks

다국어 초록 (Multilingual Abstract)

Recently, Deep learning has been used successfully to solve many recognition problems. It has many advantages over existing machine learning methods that extract feature points through hand-crafting. Deep neural networks for human activity recognition split video data into frame images, and then classify activities by analysing the connectivity of frame images according to the time. But it is difficult to apply to actual problems which has small-scale activity classes. Because this situations has a problem of overfitting and insufficient training data. In this paper, we defined 5 type of small-scale human activities, and classified them. We construct video database using 700 video clips, and obtained a classifying accuracy of 74.00%.

참고문헌 (Reference)

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