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Automatic Detection of Riots Using Deep Learning

EasyChair Preprint no. 1976, version 3

Versions: 123history
11 pagesDate: February 17, 2021

Abstract

A Riot like situation styles serious consequence on societal and individual security and a simple primary cautioning of any fierce, vehement, vicious, force-full or violent movement could significantly condense these dangers. At present, there be located oodles of video surveillance kit applied in civic places, such as bus-stations, highways, airports, Signal-squares, crossings, and railway_stations. It is of vivacious importance to examine the detrimental anomalous fillings from enormous quantities of surveillance-video data. For the thought of real-world application, this effort focuses on the challenging task of detecting violent situations in videotapes & aims to proposition a fangled way that could automatically distinguish violent behaviours by resources of computer vision methods. We take a proportion of videotapes & we train those precise sequences as violent situation & once we have got a model ready, we deploy for example on intelligent surveillance camera, any action which is close to this precise entity would be classified as violent situation and we can direct an alarm/warning back to the control-room for further necessary steps.

Keyphrases: activity recognition, Artificial Intelligence, bag of visual word, Batch Normalization, CNN RNN LSTM, Collective activity recognition, computer vision, Convolutional Neural Network, deep learning, Gaussian optical_flow Model, group activity recognition, high-level, improvised Dense Trajectories, k-nearest neighbors, Long Short-Term-storage, machine learning, Motion SIFT, neural network, neural networks, Non-violence, Optical_flow Orientation Histogram, Oriented Violent Flows, Random Forest, Recurrent_Neural_Network, Riots Detection, Space-Time Interest Points, Spacetime volume, Support_Vector_Machine, surveillance camera, surveillance video

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:1976,
  author = {Mayur Jadhav and V. A. Chakkarwar and Sharvari Tamne},
  title = {Automatic Detection of Riots Using Deep Learning},
  howpublished = {EasyChair Preprint no. 1976},

  year = {EasyChair, 2021}}
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