Integrated and deep learning–based social surveillance system : a novel approach

• Autorzy: Litoriya Ratnesh , Ramchandani Dev , Moyal Dhruvansh , Bothra Dhruv

Identyfikatory

DOI

10.14313/JAMRIS/3-2022/22

• Języki publikacji: EN

Abstrakty

EN

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 behavior. 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 same shapes or features. The primary goal of this work is to provide an integrated framework for 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.

Słowa kluczowe

EN

Video Surveillance; object detection; object tracking; YOLO v4 algorithm; OpenCV

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 3
• Strony: 30--39
• Opis fizyczny: Bibliogr. 18 poz., rys.

Twórcy

autor

Litoriya Ratnesh

• Computer Science Engineering Dept. Medi-Caps University, Indore, India

autor

Ramchandani Dev

• Computer Science Engineering Dept. Medi-Caps University, Indore, India

autor

Moyal Dhruvansh

• Computer Science Engineering Dept. Medi-Caps University, Indore, India

autor

Bothra Dhruv

• Computer Science Engineering Dept. Medi-Caps University, Indore, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).