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https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16466
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rajesh Sharma, R | - |
dc.contributor.author | Rajiv Gandhi, K | - |
dc.contributor.author | Shanmugaraja, K | - |
dc.contributor.author | Sungheetha, Akey | - |
dc.contributor.author | Chinnaiyan, R | - |
dc.contributor.author | Jegan, J | - |
dc.date.accessioned | 2024-08-29T05:41:12Z | - |
dc.date.available | 2024-08-29T05:41:12Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | pp. 1-4 | en_US |
dc.identifier.isbn | 9798350307757 | - |
dc.identifier.uri | https://doi.org/10.1109/ICIPTM59628.2024.10563768 | - |
dc.identifier.uri | https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16466 | - |
dc.description.abstract | Due 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.iso | en | en_US |
dc.publisher | 4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024 | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.subject | And Suspicious Activity Detection | en_US |
dc.subject | Convolutional Neural Networks | en_US |
dc.subject | Deep Learning | en_US |
dc.title | Motion Detection Using Heuristic Ai Based Machine Learning Approaches | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
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