Nowadays, public safety is a key concern, particularly in fast
urbanizing areas, urban development plans, and “smart city” projects
where strong security measures have been implemented due to new
security threats. The increasing trend of urbanization should be
reflected in a coordinated approach to establishing efficient urban
security systems. Current security systems have numerous constraints,
including threat identification, on-the-spot analysis, and
communication. These limitations become particularly emphasized in
extremely complex urban areas with high populations and a broad
range of activities. GuardianWatch, a groundbreaking AI-powered
surveillance system, was developed in reaction to the above-mentioned
difficulties with the primary goal of improving urban security. Real-
time monitoring, detection, and alerting capabilities for a wide range
of security concerns are precisely what GuardianWatch does. This
includes identifying firearms, detecting physical aggressions, keeping
an account of auto accidents, and identifying license plates. To achieve
these goals under various urban settings and varying lighting and
image quality issues, GuardianWatch employs a variety of cutting-edge
artificial intelligence algorithms. This all-in-one innovative platform
has tailored its algorithmic approach to the novel urban security
settings with innovative artificial intelligence algorithms including
YOLO v8, SK’s image models, TensorFlow, Haar cascades, and PyTorch.
Chinmay Dongare, Shubham Jha, Akshara Raul, Maya Patil
Mumbai University, India
GaurdianWatch, Public Security, Smart Surveillance, IDS
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 6 , Issue: 1 , Pages: 724 - 728 )
Date of Publication :
December 2024
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31
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