Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Detalhes bibliográficos
Principais autores:	Nahar, Jannatun, Promi, Zarin Tasnim, Ferdous, Jannatul, Ishrak, Fatin, Khurshid, Ridah
Outros Autores:	Chakrabarty, Dr. Amitabha
Formato:	Tese
Idioma:	en_US
Publicado em:	Brac University 2022
Assuntos:	Human Activity Recognition Deep learning DenseNet 3D bi-LSTM Spatio-temporal Violence 3D CNN TensorFlow Keras Jetson Nano Machine learning Neural networks (Computer science)
Acesso em linha:	http://hdl.handle.net/10361/17539

id	10361-17539
record_format	dspace
spelling	10361-175392022-10-26T21:01:44Z Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance Nahar, Jannatun Promi, Zarin Tasnim Ferdous, Jannatul Ishrak, Fatin Khurshid, Ridah Chakrabarty, Dr. Amitabha Department of Computer Science and Engineering, Brac University Human Activity Recognition Deep learning DenseNet 3D bi-LSTM Spatio-temporal Violence 3D CNN TensorFlow Keras Jetson Nano Machine learning Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-48). Anomalous and violent action detection has become an increasingly relevant topic and active research domain of computer vision and video processing, within the past few years. It has many proposed solutions by the researchers and this field attracted new researchers to contribute in this domain. Furthermore , the widespread use of cameras used for security purposes in big modern cities has also allowed researchers to research and examine a vast amount of information so that autonomous monitor ing can be executed. Adding effective automated violence unearthing to videotape security or multimedia content watching technologies (CCTV) would make the task of carpoolers, walk organizations, and those who are in control of social media activity monitoring much easier. We present a new deep scholarship skeleton for determining whether a videotape is violent or not, based on a suited version of DenseNet , and a bidirectional convolutional LSTM module that allows unscram bling pointed Spatio-temporal features in this paper. In addition, ablation research of the input frames was carried out, comparing thick optic outpouring and touching frames. Throughout the paper, we analyze various strategies to detect violence and their classification in use. Furthermore, in this paper, we detect violence using the Spatio-temporal feature with 3D CNN which is a DL violence detection framework, specially better for crowded places. Finally, we used embedded devices like Jetson Nano to feed with dataset and test our model and evaluate. We want a warning sent to the local police station or security agency as soon as a violent activity is detected so that urgent preventive measures can be taken. We have worked with various benchmark datasets where in one dataset, multiple models achieved a test accuracy of 100 percent, making them invincible. Furthermore, for a different dataset our models have shown 99.50% and 97.50% accuracy rates. We also did a cross dataset experiment in models which also showed pretty good results of higher than 60%. The overall results we got suggests that our system has a viable solution to anomalous behavior detection. Jannatun Nahar Zarin Tasnim Promi Jannatul Ferdous Fatin Ishrak Ridah Khurshid B. Computer Science and Engineering 2022-10-26T06:18:12Z 2022-10-26T06:18:12Z 2022 2022-01 Thesis ID: 18101291 ID: 18101589 ID: 18101565 ID: 21301716 ID: 18101683 http://hdl.handle.net/10361/17539 en_US 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. 48 Pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Human Activity Recognition Deep learning DenseNet 3D bi-LSTM Spatio-temporal Violence 3D CNN TensorFlow Keras Jetson Nano Machine learning Neural networks (Computer science)
spellingShingle	Human Activity Recognition Deep learning DenseNet 3D bi-LSTM Spatio-temporal Violence 3D CNN TensorFlow Keras Jetson Nano Machine learning Neural networks (Computer science) Nahar, Jannatun Promi, Zarin Tasnim Ferdous, Jannatul Ishrak, Fatin Khurshid, Ridah Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
author2	Chakrabarty, Dr. Amitabha
author_facet	Chakrabarty, Dr. Amitabha Nahar, Jannatun Promi, Zarin Tasnim Ferdous, Jannatul Ishrak, Fatin Khurshid, Ridah
format	Thesis
author	Nahar, Jannatun Promi, Zarin Tasnim Ferdous, Jannatul Ishrak, Fatin Khurshid, Ridah
author_sort	Nahar, Jannatun
title	Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance
title_short	Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance
title_full	Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance
title_fullStr	Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance
title_full_unstemmed	Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance
title_sort	anomalous behavior detection using spatio temporal feature and 3d cnn model for surveillance
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/17539
work_keys_str_mv	AT naharjannatun anomalousbehaviordetectionusingspatiotemporalfeatureand3dcnnmodelforsurveillance AT promizarintasnim anomalousbehaviordetectionusingspatiotemporalfeatureand3dcnnmodelforsurveillance AT ferdousjannatul anomalousbehaviordetectionusingspatiotemporalfeatureand3dcnnmodelforsurveillance AT ishrakfatin anomalousbehaviordetectionusingspatiotemporalfeatureand3dcnnmodelforsurveillance AT khurshidridah anomalousbehaviordetectionusingspatiotemporalfeatureand3dcnnmodelforsurveillance
_version_	1814309574882099200

Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance

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