Suspicious Human Activity and Fight Detection using Deep Learning


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].

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe