<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Integrated and Deep Learning–Based Social Surveillance System: a Novel Approach

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In industry and research, big data applications are gaining a lot of traction and space. Surveillance videos contribute significantly to big unlabelled data....

Integrated and Deep Learning–Based Social Surveillance System: a Novel Approach

Data publikacji: 06 wrz 2023

Zakres stron: 30 - 39

Otrzymano: 06 kwi 2022

Przyjęty: 03 maj 2022

DOI: https://doi.org/10.14313/jamris/3-2022/22

Słowa kluczowe
Video Surveillance, object detection, object tracking, YOLO v4 algorithm, OpenCV

© 2022 Ratnesh Litoriya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Integrated and Deep Learning–Based Social Surveillance System: a Novel Approach

Data publikacji: 06 wrz 2023

Zakres stron: 30 - 39

Otrzymano: 06 kwi 2022

Przyjęty: 03 maj 2022

DOI: https://doi.org/10.14313/jamris/3-2022/22

Słowa kluczoweVideo Surveillance, object detection, object tracking, YOLO v4 algorithm, OpenCV

© 2022 Ratnesh Litoriya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Video Surveillance, object detection, object tracking, YOLO v4 algorithm, OpenCV