• Tarun Kumar Sharma
• M Poonkudi
• Ayush Shekhar
• Pratik Singhal

The world we live in today has an utmost importance to have a video surveillance system for detecting any kind of violent behaviours, for example, airports, railway stations, etc. In the not so distant past, the rate of violence has increased drastically. However, the traditional violence detection system uses low-level feature extraction along with other Sapio-Temporal features models developed before in order to extract high-level features. The existing systems in place are able to detect the violent footage in videos using the traditional methods. These methods include motion regions segmentation as per the distribution of optical flow fields. This is done by using low-level features, extracted from RGB images, using the very well known "Local Histogram of Oriented Gradient", or LHOG and from optical flow images using the method of "Local Histogram of Optical Flow", or LHOF. Further in the process, the features that were extracted from the images are coded using the "Bag of Words" model, or BoW, in order to eliminate all redundant information and as a result of this step, a specific-length vector is obtained for each of the video clips and then they are classified using "Support Vector Machine", or SVM. The proposed system uses high-level feature extraction using Convolutional Neural Networks(CNN).

Violence Detection, CNN, Transfer Learning, Deep Learning, VGG16.

"Antagonism Detection In Video Sequence", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.292-296, April 2020, Available :http://www.jetir.org/papers/JETIR2004533.pdf

"Antagonism Detection In Video Sequence", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp292-296, April 2020, Available at : http://www.jetir.org/papers/JETIR2004533.pdf