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학술지 Body and Hand-Object ROI-Based Behavior Recognition Using Deep Learning
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저자
변영현, 김도형, 이재연, 곽근창
발행일
202103
출처
Sensors, v.21 no.5, pp.1-23
ISSN
1424-8220
출판사
MDPI
DOI
https://dx.doi.org/10.3390/s21051838
협약과제
21HS1500, 고령 사회에 대응하기 위한 실환경 휴먼케어 로봇 기술 개발, 이재연
초록
Behavior recognition has applications in automatic crime monitoring, automatic sports video analysis, and context awareness of so-called silver robots. In this study, we employ deep learning to recognize behavior based on body and hand?뱋bject interaction regions of interest (ROIs). We propose an ROI-based four-stream ensemble convolutional neural network (CNN). Behavior recognition data are mainly composed of images and skeletons. The first stream uses a pre-trained 2D-CNN by converting the 3D skeleton sequence into pose evolution images (PEIs). The second stream inputs the RGB video into the 3D-CNN to extract temporal and spatial features. The most important information in behavior recognition is identification of the person performing the action. Therefore, if the neural network is trained by removing ambient noise and placing the ROI on the person, feature analysis can be performed by focusing on the behavior itself rather than learning the entire region. Therefore, the third stream inputs the RGB video limited to the body-ROI into the 3D-CNN. The fourth stream inputs the RGB video limited to ROIs of hand?뱋bject interactions into the 3D-CNN. Finally, because better performance is expected by combining the information of the models trained with attention to these ROIs, better recognition will be possible through late fusion of the four stream scores. The Electronics and Telecommunications Research Institute (ETRI)-Activity3D dataset was used for the experiments. This dataset contains color images, images of skeletons, and depth images of 55 daily behaviors of 50 elderly and 50 young individuals. The experimental results showed that the proposed model improved recognition by at least 4.27% and up to 20.97% compared to other behavior recognition methods.
키워드
Behavior recognition, Convolutional neural network, Ensemble, RGB video, Skeleton
KSP 제안 키워드
3d Skeleton, Color images, Context awareness, Convolution neural network(CNN), Depth image, Feature Analysis, Learning Behavior, Proposed model, Recognition method, Regions of interest, Research institute
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