Authors :
Digambar Kauthkar; Snehal Pingle; Vijay Bansode; Pooja Idalkanthe; Sunita Vani
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3ulJOhE
DOI :
https://doi.org/10.5281/zenodo.6791644
Abstract :
With the increasing number of shootings, knife
attacks, terrorist attacks etc. in public places across the
world, identifying the wrong behavior of human activities
in public places has become an important task. This paper
focuses on a deep learning approach to detect suspicious
human activity and fight using convolutional neural
networks from images and videos. We analyze different
CNN architectures and compare their accuracy. We
design our systems that can process video footage from
cameras in real time and predict whether activity is
suspicious or fight found or not. We also propose future
developments that can be undertaken to detect and
counter distrustful human activity in the public region.
Keywords :
Recognizing Human Suspicious Activity, Fight Detection, [CNN Model, Deep Learning].
With the increasing number of shootings, knife
attacks, terrorist attacks etc. in public places across the
world, identifying the wrong behavior of human activities
in public places has become an important task. This paper
focuses on a deep learning approach to detect suspicious
human activity and fight using convolutional neural
networks from images and videos. We analyze different
CNN architectures and compare their accuracy. We
design our systems that can process video footage from
cameras in real time and predict whether activity is
suspicious or fight found or not. We also propose future
developments that can be undertaken to detect and
counter distrustful human activity in the public region.
Keywords :
Recognizing Human Suspicious Activity, Fight Detection, [CNN Model, Deep Learning].
