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학술지 Action Recognition Using Close-Up of Maximum Activation and ETRI-Activity3D LivingLab Dataset
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김도영, 이인웅, 김도형, 이상훈
Sensors, v.21 no.20, pp.1-21
21HS1500, 고령 사회에 대응하기 위한 실환경 휴먼케어 로봇 기술 개발, 이재연
The development of action recognition models has shown great performance on various video datasets. Nevertheless, because there is no rich data on target actions in existing datasets, it is insufficient to perform action recognition applications required by industries. To satisfy this requirement, datasets composed of target actions with high availability have been created, but it is difficult to capture various characteristics in actual environments because video data are generated in a specific environment. In this paper, we introduce a new ETRI-Activity3D-LivingLab dataset, which provides action sequences in actual environments and helps to handle a network generalization issue due to the dataset shift. When the action recognition model is trained on the ETRI-Activity3D and KIST SynADL datasets and evaluated on the ETRI-Activity3D-LivingLab dataset, the performance can be severely degraded because the datasets were captured in different environments domains. To reduce this dataset shift between training and testing datasets, we propose a close-up of maximum activation, which magnifies the most activated part of a video input in detail. In addition, we present various experimental results and analysis that show the dataset shift and demonstrate the effectiveness of the proposed method.
KSP 제안 키워드
Action recognition, Dataset shift, High availability, Recognition model, Video data, different environments, rich data, specific environment, training and testing
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