A real time crime scene intelligent video surveillance systems in violence detection framework using deep learning techniques. (October 2022)

• Record Type: Journal Article
• Authors: Sahay, Kishan Bhushan; Balachander, Bhuvaneswari; Jagadeesh, B.; Anand Kumar, G.; Kumar, Ravi; Rama Parvathy, L.
• Is Part Of: Computers & electrical engineering. Volume 103(2022)
• Publication Date: 2022-10
• Subjects: Surveillance system -- Detecting crime scene video -- Deep learning -- ST -- DRNN; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108319
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Currently tremendous growth is observed in research of surveillance system. The surveillance cameras installed at various public places like offices, hospitals, schools, highways, etc. This research proposed novel technique in detecting crime scene video surveillance system in real time violence detection using deep learning architectures. Its purpose is to detect signals of hostility and violence in real time, allowing abnormalities to be distinguished from typical patterns. Abstract: Surveillance system research is now experiencing great expansion. Surveillance cameras put in public locations such as offices, hospitals, schools, roads, and other locations can be utilised to capture important activities and movements for event prediction, online monitoring, goal-driven analysis, and intrusion detection. This research proposed novel technique in detecting crime scene video surveillance system in real time violence detection using deep learning architectures. Here the aim is to collect the real time crime scene video of surveillance system and extract the features using spatio temporal (ST) technique with Deep Reinforcement neural network (DRNN) based classification technique. The input video has been processed and converted as video frames and from the video frames the features has been extracted and classified. Its purpose is to detect signals of hostility and violence in real time, allowing abnormalities to be distinguished from typical patterns. To validate our system's performance, it is trained as well as tested in large-scale UCF Crime anomaly dataset. The experimental results reveal that the suggested technique performs well in real-time datasets, with accuracy of 98%, precision of 96%, recall of 80%t, and F-1 score of 78%. Graphical abstract: Image, graphical abstract