Detection of violent activity in surveillance system using different deep learning techniques
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
| Principais autores: | , , , |
|---|---|
| Outros Autores: | |
| Formato: | Tese |
| Idioma: | English |
| Publicado em: |
Brac University
2024
|
| Assuntos: | |
| Acesso em linha: | http://hdl.handle.net/10361/23573 |
| id |
10361-23573 |
|---|---|
| record_format |
dspace |
| spelling |
10361-235732024-06-25T21:00:58Z Detection of violent activity in surveillance system using different deep learning techniques Chakravorty, Tirthendu Prosad Abeer, Mobashra Baroi, Shaiane Prema Roy, Sristy Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University Violent activity Surveillance system Activity recognition Deep learning Neural network Neural networks (Computer science) Data mining This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 61-64). In the past decade, surveillance cameras have been a necessary integration for security measures in all types of localities. The omnipresence of these devices has substantially aided in tackling violent criminal activities. In larger systems, continuous manual monitoring becomes a cumbersome task and often causes delayed response. Therefore, automated recognition of aggressive activities in surveillance systems can enhance the remote monitoring experience and increase the preciseness of response. Previous experiments on various deep-learning techniques and Convolutional Neural Networks (CNN) have tackled the challenge by identifying potential violent activities in real-time with good accuracy. The aim of this research is to benefit from reduced computational cost while maintaining optimality for practical implementation in real life. Hence, in this study, preliminarily a lightweight yet highly effective CNN model has been proposed that extracts spatial features by 2D convolutions. Later on several custom models based on combinations of CNN and RNN architectures have been developed for spatio-temporal features from the videos. The models have undergone robust tuning and training and are capable of accurately extracting frame-level and temporal-level features based on the architectural types. They have been then conclusively evaluated on a combination of multiple benchmark datasets to compare how well each of them performs. In conclusion, the proposed spatial feature-based model obtained an outstanding test accuracy of 99.6% and the best spatio-temporal feature-based model in terms of performance attained a test accuracy of 98.75%. Tirthendu Prosad Chakravorty Mobashra Abeer Shaiane Prema Baroi Sristy Roy B.Sc in Computer Science 2024-06-25T05:51:30Z 2024-06-25T05:51:30Z ©2023 2023-09 Thesis ID 19201036 ID 19201092 ID 21101098 ID 20101202 http://hdl.handle.net/10361/23573 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. 74 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Violent activity Surveillance system Activity recognition Deep learning Neural network Neural networks (Computer science) Data mining |
| spellingShingle |
Violent activity Surveillance system Activity recognition Deep learning Neural network Neural networks (Computer science) Data mining Chakravorty, Tirthendu Prosad Abeer, Mobashra Baroi, Shaiane Prema Roy, Sristy Detection of violent activity in surveillance system using different deep learning techniques |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Karim, Dewan Ziaul |
| author_facet |
Karim, Dewan Ziaul Chakravorty, Tirthendu Prosad Abeer, Mobashra Baroi, Shaiane Prema Roy, Sristy |
| format |
Thesis |
| author |
Chakravorty, Tirthendu Prosad Abeer, Mobashra Baroi, Shaiane Prema Roy, Sristy |
| author_sort |
Chakravorty, Tirthendu Prosad |
| title |
Detection of violent activity in surveillance system using different deep learning techniques |
| title_short |
Detection of violent activity in surveillance system using different deep learning techniques |
| title_full |
Detection of violent activity in surveillance system using different deep learning techniques |
| title_fullStr |
Detection of violent activity in surveillance system using different deep learning techniques |
| title_full_unstemmed |
Detection of violent activity in surveillance system using different deep learning techniques |
| title_sort |
detection of violent activity in surveillance system using different deep learning techniques |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23573 |
| work_keys_str_mv |
AT chakravortytirthenduprosad detectionofviolentactivityinsurveillancesystemusingdifferentdeeplearningtechniques AT abeermobashra detectionofviolentactivityinsurveillancesystemusingdifferentdeeplearningtechniques AT baroishaianeprema detectionofviolentactivityinsurveillancesystemusingdifferentdeeplearningtechniques AT roysristy detectionofviolentactivityinsurveillancesystemusingdifferentdeeplearningtechniques |
| _version_ |
1814307158218506240 |