인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract To accurately detect the abnormal behavior of business hall employees in the indoor working environment and ensure the normal operation of the store, a deep learning technology is combined to build an audio and video feature extraction network. First, a separate video and an audio feature extraction model are built by using a convolutional neural network and a long short-term memory network, and then an abnormal behavior detection model of audio and video feature fusion is built using multi-channel modules. This model can divide audio and video data into multiple different channels, and perform feature extraction and recognition on each channel. To assess the performance and robustness of the proposed model, experiments are organized on multiple datasets. The research outcomes denote that compared with some existing neural network models, the detection accuracy, recall, F1 value, and mean average precision values of the proposed model in the video audio dataset are 97.85, 96.98, 95.61, and 35.61%, respectively. The research method has a remarkable fusion effect on video and audio datasets, which improves the accuracy and robustness of abnormal behavior recognition. In addition, user and expert satisfaction with the model has also improved significantly, reaching 0.92 and 0.97, respectively. The novelty of this study lies in the comprehensive analysis of audio and video data by using multi-channel processing technology. Through effective extraction and fusion of video frames and audio signals at the same time, it significantly improves the detection ability of abnormal behaviors in indoor environments in complex scenes.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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