Enhancing Surveillance Security Through Violence Detection Using Advanced Deep Learning Algorithms

Negi, Ashish (2024) Enhancing Surveillance Security Through Violence Detection Using Advanced Deep Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

Detection of violent activities is a prime importance in terms of public safety, security monitoring, and law enforcement support. The growing dependence on extensive surveillance systems in the public and private domains made it necessary to ascertain these violent acts in real-time, which is quite a challenge. Violence detection in surveillance videos becomes a critical task with public safety, law enforcement, and security monitoring applications. Though quite challenging, real-time detection of violent activities remains difficult to accomplish due to the dynamic nature of video data, constraints on computational efficiency, and the need to be accurate across diverse situations. Existing solutions mostly rely on traditional and special techniques of computer vision or single deep learning models, which can get bogged down while performing both tasks of higher computation efficiency and accuracy in a complex environment. This paper presents a comprehensive framework that harnesses advanced deep learning algorithms: Dense Neural Networks, Long Short Term Memory, Gated Recurrent Units, and a hybrid LSTM+GRU model, for the task. Our methodology combines spatial and sequential feature extraction from video frames, preprocessing, data augmentation, and model training. Evaluation of these models is performed using accuracy, precision, recall, F1-score, AUC, and loss to identify the best model. The GRU model outperformed all, achieving slightly better accuracy and generalization, making it the best possible solution for any real-life application. As a practical application, we have developed a Flask-based web application so that users can upload videos, which could lead to detecting violent activities.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Anant, Aaloka UNSPECIFIED
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare > Criminology > Crimes and Offences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Q Science > Q Science (General) > Research > Research--Equipment and Supplies > Scientific apparatus and instruments > Physical instruments > Detectors > Remote sensing > Electronic surveillance > Video surveillance Z Bibliography. Library Science. Information Resources > ZA Information resources > Research > Research--Equipment and Supplies > Scientific apparatus and instruments > Physical instruments > Detectors > Remote sensing > Electronic surveillance > Video surveillance
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 15:07
Last Modified:	03 Sep 2025 15:07
URI:	https://norma.ncirl.ie/id/eprint/8759

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