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- W3209969175 abstract "Safety and health security are the major concerns in today's modern world. Most of the countries have adopted camera surveillance systems to achieve a secure environment, one such example is restriction imposed for the movement of people during the COVID-19 pandemic. Thus, surveillance systems serve the purpose of humans to identify intruders with suspicious behavior. Detecting these intrusions or any suspicious events in an early stage from surveillance systems is an important and challenging task. This can be done using Suspicious Event Detection Models (SEDM) and tools. In earlier systems, it is found that machine learning methods are proved to be efficient in predicting the suspicion activities. In this chapter, a survey of various SEDM Strategies and Tools that were developed earlier which captures the suspicious events in campuses or societies is discussed that provides a Full-proof secure environment. Few, earlier SEDM have also used deep learning approaches, IoT, and fuzzy logic techniques. Finally, an improved SEDM for campuses based on deep learning is suggested. The capability of deep learning (CNN) method is very influential in extraction of features from unstructured contents, especially from captured images of video. The efficiency of this suggested SEDM will be better when compared with earlier state-of-art-systems, which do not support alarming system by making use of GPS, and then extracting personal details." @default.
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- W3209969175 date "2021-11-03" @default.
- W3209969175 modified "2023-09-24" @default.
- W3209969175 title "Strategies and Tools for Effective Suspicious Event Detection from Video: A Survey Perspective (COVID-19)" @default.
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- W3209969175 doi "https://doi.org/10.1007/978-981-16-5411-4_7" @default.
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