Intelligent User Pattern Recognition based on Vision, Audio and Activity for Abnormal Event Detections of Single Households

JungJuHo; Ahn Junho

doi:10.9708/jksci.2019.24.05.059

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

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* 2021년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

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이 논문은 한국연구재단의 지원을 받아 작성되었습니다.

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