Violent Event Recognition and Monitoring Using Deep Learning for Surveillance Videos

Year : 2024 | Volume :02 | Issue : 02 | Page : 39-44
By

Vedangi Raut

Rutvik Redkar

  1. Research Scholar MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai Maharashtra India
  2. Research Scholar MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai Maharashtra India

Abstract

The significance of real-time capabilities in human detection and tracking is discussed in the abstract of the paper. We talk about tracking, eye detection, and face detection. A thorough motion detection program for use in video monitoring and other applications is suggested by the study. The goal of the study is to further human tracking technology. Optical flow features and appearance-invariant features from a Darknet CNN model are integrated. Acquiring Knowledge of Complicated Sequences: In order to identify complicated activity sequences for ultimate violence detection, a Long Short-Term Memory (LSTM) network is utilized, which enables the system to identify long-term patterns. Thorough Evaluation: The approach outperforms current methods and provides a baseline for violence detection systems when tested in a variety of inside and outdoor surveillance scenarios. The study provides an overview of classical and deep learning-based forms of violence.

Keywords: Convolution neural network (CNN), long short-term memory, violence, models, datasets

[This article belongs to International Journal of Advanced Robotics and Automation Technology(ijarat)]

How to cite this article: Vedangi Raut, Rutvik Redkar. Violent Event Recognition and Monitoring Using Deep Learning for Surveillance Videos. International Journal of Advanced Robotics and Automation Technology. 2024; 02(02):39-44.
How to cite this URL: Vedangi Raut, Rutvik Redkar. Violent Event Recognition and Monitoring Using Deep Learning for Surveillance Videos. International Journal of Advanced Robotics and Automation Technology. 2024; 02(02):39-44. Available from: https://journals.stmjournals.com/ijarat/article=2024/view=144390





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Regular Issue Subscription Review Article
Volume 02
Issue 02
Received March 11, 2024
Accepted March 26, 2024
Published April 25, 2024