Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16466
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dc.contributor.authorRajesh Sharma, R-
dc.contributor.authorRajiv Gandhi, K-
dc.contributor.authorShanmugaraja, K-
dc.contributor.authorSungheetha, Akey-
dc.contributor.authorChinnaiyan, R-
dc.contributor.authorJegan, J-
dc.date.accessioned2024-08-29T05:41:12Z-
dc.date.available2024-08-29T05:41:12Z-
dc.date.issued2024-
dc.identifier.citationpp. 1-4en_US
dc.identifier.isbn9798350307757-
dc.identifier.urihttps://doi.org/10.1109/ICIPTM59628.2024.10563768-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16466-
dc.description.abstractDue to the rise in shootings, knife assaults, terrorist attacks, etc. that occur in public spaces around the world, it has become crucial to identify suspicious activity in these areas. This study employs convolutional neural networks and deep learning to identify suspicious activity in videos and photos. We examine various CNN architectures and contrast their precision. We describe the design of our system, which can analyze live video feed from cameras and determine whether an activity is suspicious or not. We also make suggestions for potential future advancements in the field of detecting suspicious activity. © 2024 IEEE.en_US
dc.language.isoenen_US
dc.publisher4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectAnd Suspicious Activity Detectionen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectDeep Learningen_US
dc.titleMotion Detection Using Heuristic Ai Based Machine Learning Approachesen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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