Social Distancing Through Image Processing, Video Analysis, and CNN

Bari, A. S. M. Hossain; Mohammed, Sharfuddin Waseem; Sameena,; Sharanya,

doi:10.1007/978-981-16-4103-9_6

Cited by 1 publication

(1 citation statement)

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“…Block diagram of methodology component that focuses on establishing a person's position from the input frame. As a result, selecting the most appropriate object detection model is critical in order to prevent any issues with recognizing people [15].…”

Section: Methodsmentioning

confidence: 99%

Integrated and Deep Learning Based Social Surveillance System: A Novel Approach

Litoriya,

Ramchandani,

Moyal

et al. 2023

JAMRIS

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In industry and research, big data applications are gaining a lot of traction and space. Surveillance videos contribute significantly to big unlabelled data. The aim of visual surveillance is to understand and determine object behaviour. It includes static and moving object detection, as well as video tracking to comprehend scene events. Object detection algorithms may be used to identify items in any video scene. Any video surveillance system faces a significant challenge in detecting moving objects and differentiating between objects with the same shapes or features. The primary goal of this work is to provide an integrated framework for a quick overview of video analysis utilizing deep learning algorithms to detect suspicious activity. In greater applications, the detection method is utilized to determine the region where items are available and the form of objects in each frame. This video analysis also aids in the attainment of security. Security may be characterized in a variety of ways, such as identifying theft or violation of covid protocols. The obtained results are encouraging and superior to existing solutions with 97% accuracy.

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Section: Methodsmentioning

confidence: 99%