Efficient fall detection based on event pattern matching in image streams = 이미지 스트림에서의 이벤트 패턴 매칭에 기반한 효율적인 낙상 감지

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This research proposes sliding window fall detection match (SW-FDM), a rule-based fall detection method based on event pattern matching from the human body posture image streams. Fall and post-fall (long lie) rules are expressed as a pattern, and complex event processing (CEP) systems is required to process them in a temporal ordering relationship as well as in a large scale of streams. Those patterns can be detected with event selection strategies such as Skip Till Next Match, and Skip Till Any Match. However, existing strategies generate either duplicate or missing alarms. In addition, processing cost is a severe problem when the size of event streams is large. SW-FDM applies a concept of sliding window, so it is able to detect correct matches constantly, and it reduces processing cost without a duplicate computation. The experiment proved that SW-FDM results in a more accurate and efficient performance. Also, it was shown that the improvement of efficiency becomes greater as an increasingly larger size of data sources are sent to the implemented CEP systems.
Advisors
Lee, Jae-Gilresearcher이재길researcher
Description
한국과학기술원 :지식서비스공학대학원,
Publisher
한국과학기술원
Issue Date
2016
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 지식서비스공학대학원, 2016.8 ,[iii, 47 p. :]

Keywords

Camera-Based Fall Detection; Complex Event Processing; Event Selection Strategy; Event Pattern Matching; Sliding Window; 카메라 기반 낙상 감지; 복합 이벤트 처리; 이벤트 선택 전략; 이벤트 패턴 매칭; 슬라이딩 윈도우

URI
http://hdl.handle.net/10203/221984
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=663511&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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