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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Finding Real-Time Crime Detections during Video Surveillance by Live CCTV Streaming Using the Deep Learning Models Subject The topic of the resource Classification; Deep Learning; live CCTV; Real-Time crime detection (RCV); Video Surveillance Description An account of the resource Nowadays, securing people in public places is an emerging social issue in the research of real-Time crime detection (RCD) by video surveillance, in which initial automatic recognition of suspicious objects is considered a prime problem in RCD. Dynamic live CCTV monitoring and finding real-Time crime activities by detecting suspicious objects is required to prevent unusual activities in public places. Continuous live CCTV video surveillance of objects and classification of suspicious activities are essential for real-Time crime detection. Deep training models have greatly succeeded in image and video classifications. Thus, this paper focuses on the use of trustworthy deep learning models to intelligently classify suspicious objects to detect real-Time crimes during live video surveillance by CCTV. In the experimental study, various convolutional neural network (CNN) models are trained using real-Time crime and non-crime videos. Three performance parameters, accuracy, loss, and computational time, are estimated for three variants of CNN models for the real-Time crime classifications. Three categories of videos, i.e., crime video (CV), non-crime video (NCV), and weapon-crime video (WCV), are used in the training of three deep models, CNN, 3D CNN, and Convolutional Long short-Term memory (ConvLSTM). The ConvLSTM scored higher accuracy, lower loss values, and runtime efficiency than CNN and 3D CNN when detecting real-Time crimes. 2024 ACM. Creator An entity primarily responsible for making the resource Poonia R.C.; Upreti K.; Prabu P.; Rajendra Prasad K. Source A related resource from which the described resource is derived ACM International Conference Proceeding Series, pp. 272-277. Publisher An entity responsible for making the resource available Association for Computing Machinery Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1145/3669754.3669794" target="_blank" rel="noreferrer noopener">https://doi.org/10.1145/3669754.3669794</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203828349&amp;doi=10.1145%2F3669754.3669794&amp;partnerID=40&amp;md5=e038488dd6b27ac09fd3edc356e967f4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203828349&amp;doi=10.1145%2f3669754.3669794&amp;partnerID=40&amp;md5=e038488dd6b27ac09fd3edc356e967f4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-840071705-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper 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, Delhi NCR, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR, India; Prabu P., Department of Computer Science, Christ University, Delhi NCR, India; Rajendra Prasad K., Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Telangana, Hyderabad, India