Violent activity detection through surveillance camera using deep learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliografiska uppgifter
Huvudupphovsmän:	Miah, Parvez, Haque, Abrar Ahbabul, Imran, Abdullah Al, Hassan, MD. Radip, Rahman, Rafiur
Övriga upphovsmän:	Alam, Md. Golam Rabiul
Materialtyp:	Lärdomsprov
Språk:	English
Publicerad:	Brac University 2023
Ämnen:	Deep learning MobileNet-V2 ResNet50 Surveillance camera Security Suspicious activity CNN LSTM Grad-CAM Machine learning Cognitive learning theory
Länkar:	http://hdl.handle.net/10361/18331

id	10361-18331
record_format	dspace
spelling	10361-183312023-05-25T21:01:46Z Violent activity detection through surveillance camera using deep learning Miah, Parvez Haque, Abrar Ahbabul Imran, Abdullah Al Hassan, MD. Radip Rahman, Rafiur Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Deep learning MobileNet-V2 ResNet50 Surveillance camera Security Suspicious activity CNN LSTM Grad-CAM Machine learning Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 35-36). Surveillance camera systems have been implemented in most parts of the world to combat the rising rate of criminal activities. In the hopes of making public places safer for everyone, computer vision has also aided in making these systems more sophisticated but reliable and efficient. However, we are yet to make them better. While modern systems are able to record incidents, they often do not do so intelligently in order to make it easier for law reinforcements to respond quickly enough to aid victims or stop more crimes from occurring. Hence for our thesis project, we intend to use computer vision on a surveillance system so that it is able to identify crimes such as physical altercation, harassment, hijacking, snatching, etc. In this model, an action recognition system will be used, where we will be using extracted images from video feeds from multiple sources, and all those sources (cameras) will be centrally connected to a server. The server will be connected to databases containing information about violent activities. Based on the feeds, a signal will be sent to the respective system if a particular activity is detected. This system is mainly based on image processing concepts using different neural networks like MobileNet-V2, ResNet50, and LSTM to match live images with the existing trained system. This model will specifically use to detect criminal activities such as punching, kicking, slapping, and weapon violence, and all these pieces of information will be previously stored in the database. We have also implemented Grad-CAM in an effort to apply model explain ability. Parvez Miah Abrar Ahbabul Haque Abdullah Al Imran MD. Radip Hassan Rafiur Rahman B. Computer Science 2023-05-25T10:06:58Z 2023-05-25T10:06:58Z 2023 2023-01 Thesis ID 19101339 ID 19301040 ID 19101482 ID 19101524 ID 19101461 http://hdl.handle.net/10361/18331 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 36 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning MobileNet-V2 ResNet50 Surveillance camera Security Suspicious activity CNN LSTM Grad-CAM Machine learning Cognitive learning theory
spellingShingle	Deep learning MobileNet-V2 ResNet50 Surveillance camera Security Suspicious activity CNN LSTM Grad-CAM Machine learning Cognitive learning theory Miah, Parvez Haque, Abrar Ahbabul Imran, Abdullah Al Hassan, MD. Radip Rahman, Rafiur Violent activity detection through surveillance camera using deep learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Miah, Parvez Haque, Abrar Ahbabul Imran, Abdullah Al Hassan, MD. Radip Rahman, Rafiur
format	Thesis
author	Miah, Parvez Haque, Abrar Ahbabul Imran, Abdullah Al Hassan, MD. Radip Rahman, Rafiur
author_sort	Miah, Parvez
title	Violent activity detection through surveillance camera using deep learning
title_short	Violent activity detection through surveillance camera using deep learning
title_full	Violent activity detection through surveillance camera using deep learning
title_fullStr	Violent activity detection through surveillance camera using deep learning
title_full_unstemmed	Violent activity detection through surveillance camera using deep learning
title_sort	violent activity detection through surveillance camera using deep learning
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/18331
work_keys_str_mv	AT miahparvez violentactivitydetectionthroughsurveillancecamerausingdeeplearning AT haqueabrarahbabul violentactivitydetectionthroughsurveillancecamerausingdeeplearning AT imranabdullahal violentactivitydetectionthroughsurveillancecamerausingdeeplearning AT hassanmdradip violentactivitydetectionthroughsurveillancecamerausingdeeplearning AT rahmanrafiur violentactivitydetectionthroughsurveillancecamerausingdeeplearning
_version_	1814308393782870016

Violent activity detection through surveillance camera using deep learning

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