Abstract
There is a need for developing deep learning solutions to analyze videos to
identify any violence being present. This paper proposes a method for the detection of
the presence of violent activities in videos using Deep Neural Networks. Recently there
has been a rapid development happening in the field of Deep Neural networks, but the
number of solutions that have been developed for violence detection is very few. The
proposed solution will play a major role in transforming the way law enforcement
works and support the government’s initiative to make cities smarter. The model is
built using CNN for video frame feature extraction and LSTM to capture localized
features present in the video frames. The LSTM extracts the localized features using
the spatiotemporal relationship between the video frames. The local motion present in
the video is analyzed. This work focuses on accuracy and fast response time. The
performance was evaluated on the hockey fight dataset to detect violent activities.
Keywords: CNN, LSTM, Violence detection.
Related Journals
Recent Patents on Computer Science
Current E-Learning
Current Computer Science
International Journal of Sensors, Wireless Communications and Control
Recent Advances in Computer Science and Communications
Journal of Fuzzy Logic and Modeling in Engineering
The Chinese Journal of Artificial Intelligence
Current Chinese Computer Science
Current Artificial Intelligence
Related Books
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
A First Course in Artificial Intelligence
Artificial Intelligence: Models, Algorithms and Applications
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
Handbook of Mobile Application Development: A Guide to Selecting the Right Engineering and Quality Features
Applications of Modern High Performance Networks
Arduino Meets Matlab: Interfacing, Programs and Simulink
Arduino and SCILAB based Projects
Application of Chaos and Fractals to Computer Vision