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In 2001, the Atomic Weapons Establishment exceeded management targets for "abnormal events", including accidents involving nuclear material at Aldermaston and the neighbouring Burghfield bomb assembly plant.
The pilots looked through the handbook containing checklists for abnormal events, as the jet incorrectly alerted pilots it was in a stall, pushing the nose down – an automated response built into the software as part of the maneuvering characteristics augmentation system (MCAS) anti-stall program in the 737 Max.
At the energy ratio of 0 dB which represent real-world conditions appropriately, the proposed system demonstrates high performance with average EER of 6.68%, average miss detection rate of 16.4% and average false alarm probability for abnormal events of 2.26% which is of severe importance for this kind of applications.
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Experimental results show that the deep model is effective for abnormal event detection in video surveillance.
We propose a macro-observation scheme for abnormal event detection in daily life.
More information on human action recognition techniques for abnormal event detection can be found in [102].
For an intelligent video surveillance system, the detection of a human being is important for abnormal event detection, human gait characterization, people counting, person identification and tracking, pedestrian detection, gender classification, fall detection of elderly people, etc.
An algorithm for detection of abnormal events using the defined classifier is also proposed.
This paper describes the safety analyses of abnormal events for the SWFR.
The first limitation is the statistical analysis used for the detection of abnormal events.
Thus, a surveillance platform that will merge all of the surveillance activities and will contain stronger statistical tools for the surveillance of abnormal events is under development.
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