Affiliations: [a] Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India
| [b] Department of Electronics & Communication Engineering, Malaviya National Institute of Technology, Jaipur, India
| [c] Department of Information Technology, Delhi Technological University, Delhi, India
Correspondence:
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Corresponding author. Dinesh Kumar Vishwakarma, Department of Information Technology, Delhi Technological University, Delhi, 110042, India. Tel.: +91 09971339840; E-mail: dinesh@dtu.ac.in.
Abstract: Violence detection is a challenging task in the computer vision domain. Violence detection framework depends upon the detection of crowd behaviour changes. Violence erupts due to disagreement of an idea, injustice or severe disagreement. The aim of any country is to maintain law and order and peace in the area. Violence detection thus becomes an important task for authorities to maintain peace. Traditional methods have existed for violence detection which are heavily dependent upon hand crafted features. The world is now transitioning in to Artificial Intelligence based techniques. Automatic feature extraction and its classification from images and videos is the new norm in surveillance domain. Deep learning platform has provided us the platter on which non-linear features can be extracted, self-learnt and classified as per the appropriate tool. One such tool is the Convolutional Neural Networks, also known as ConvNets, which has the ability to automatically extract features and classify them in to their respective domain. Till date there is no survey of deciphering violence behaviour techniques using ConvNets. We hope that this survey becomes an exclusive baseline for future violence detection and analysis in the deep learning domain.
