Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Poonia, Ramesh Chandra; Upreti, Kamal; Singh, Nidhi; Kesarwani, Jyoti; Alam, Mohammad Shabbir Title A name given to the resource An Efficient Detection of Suspicious Objects from Dynamic Video Surveillance by Fusion-based Multiview Deep Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Mobile Multimedia;Volume;21;Issue;1;pp.1-26 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13052/jmm1550-4646.2111" target="_blank" rel="noreferrer noopener">https://doi.org/10.13052/jmm1550-4646.2111</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003927796?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003927796?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Poonia R.C., Department of Computer Science, Christ University, NCR, Delhi, India; Upreti K., Department of Computer Science, Christ University, NCR, Delhi, India; Singh N., G. D. Goenka University, Haryana, Gurgaon, India; Kesarwani J., United College of Engineering and Research, Uttar Pradesh, Prayagraj, India; Alam M.S., Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia Description An account of the resource Real-time detections of suspicious objects are needed to identify for finding criminal activities and are used in immediate alert systems for public safety applications. Video surveillance systems use live, closed-circuit televisions (live CCTVs) for dynamic video capturing of objects. Finding criminal activities over the dynamic video data is an emerging surveillance problem. The deep learning techniques are tedious for detecting suspicious movable objects and criminal activities. YOLO (You Only Look Once) gives more prominent movable video object detection accuracy than conventional deep models, like Convolutional Neural Network (CNN), 3D CNN, and Convolutional LSTM. State-of-the-art YOLO models, YOLOv8n, YOLOv8s, and YOLOv8l, are emphasized for extracting and detecting object motion detection from the dynamic video. YOLO models use single-view deep learning to classify or detect objects. These models limit the accuracy of the detection of complex and dynamic objects of dynamic video data. This paper presents the Fusion-based Multiview deep learning techniques to overcome this issue. The experimental study demonstrates that the proposed methodology efficiently detects suspicious data objects more than the single-view deep models. 2025 River Publishers. Subject The topic of the resource Deep learning; fusion; object detection; video surveillance; YOLO Publisher An entity responsible for making the resource available River Publishers Relation A related resource ISSN: 15504646; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online