2023
DOI: 10.48084/etasr.5739
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Recognition of Suspicious Human Activity in Video Surveillance: A Review

Abstract: Over the past few years, there has been a noticeable growth in the use of video surveillance systems, frequently functioning as integrated systems that remotely monitor key locations. In order to prevent terrorism, theft, accidents, illegal parking, vandalism, fighting, chain snatching, and crime, human activities can be observed through visual surveillance in sensitive and public places like buses, trains, airports, banks, shopping centers, schools, and colleges. In this paper, a review of the state-of-the-ar… Show more

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Cited by 4 publications
(2 citation statements)
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“…MobileNetV3-Large is more accurate than ImageNet with less latency than MobileNetV2 [10]. In [16][17][18][19][20][21][22][23][24][25][26][27] one can review some machine learning models that show its importance and improved results.…”
Section: A Modelsmentioning
confidence: 99%
“…MobileNetV3-Large is more accurate than ImageNet with less latency than MobileNetV2 [10]. In [16][17][18][19][20][21][22][23][24][25][26][27] one can review some machine learning models that show its importance and improved results.…”
Section: A Modelsmentioning
confidence: 99%
“…Human activity tracking based on computer vision has been widely used, but infrastructure support is required to implement such systems, e.g. mounting some cameras in the monitoring areas [2]. Alternatively, inertial sensors available in smartphones-such as accelerometers and gyroscopes-can be worn on the body to measure acceleration and orientation [3].…”
Section: Introductionmentioning
confidence: 99%