Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The global crime rate has been increasing in terms of
numbers, with an estimated hundred million offences committed
each year. Surveillance cameras have been installed at every street
corner to capture and record such activities. Detecting such acts
in time might reduce a person’s potential danger. It is difficult for
human supervision of such systems to discover such behaviours
on the spot for every surveillance CCTV video. In order to
discover such actions as soon as possible, this study proposes an
intelligence surveillance system that recognises criminal scenes
and shows crime activity for a recorded surveillance video using
a deep learning approach. As a result, the suggested systems
employ deep learning techniques CNN and LSTM to categorise
and detect odd actions for recorded data.The Convolution neural
network is used to learn and train the data.
Keywords:
Suspicious Activity,CNN,Lstm,Recorded data
Cite Article:
"Suspecting Abnormal Activities Under Survilleance", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 3, page no.437 - 441, March-2023, Available :http://www.ijrti.org/papers/IJRTI2303075.pdf
Downloads:
000205137
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator