Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM
Abstract Football is the most popular sport in the world with four billion fans all over the world. Reportedly, the violence incidence rates are high during or after the matches. The violent or destructive behavior carried out by a person or player, who watches or plays the game in the stadium is known as football hooliganism. To prevent or control the violence, a real time violence detection system is exclusively needed to monitor the behavior of the crowd and players to take necessary action before the violence is about to happen. Even it is necessary for the system to find whether the attack is non-intentional or intentional in the game. In this paper, a real time violence detection system is proposed which processes the huge input streaming data and recognize the violence with human intelligence simulation. The input to the system is the enormous amount of real time video streams from different sources which is processed in Spark framework. In the Spark framework, the frames are separated and the features of individual frames are extracted by using HOG (Histogram of Oriented Gradients) function. Then the frames are labeled based on features as violence model, human part model and negative model, which are used to train the Bidirectional Long Short-Term Memory (BDLSTM) network for recognition of violence scenes. The bidirectional LSTM can access the information both in forward and reverse direction. Thus the output is generated in context to both past and future information. The network is trained with the violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. Moreover to make the model robust to violence detection, we have created a dataset with 410 video clips having non-violence scenes and 409 video clips having violence scenes, acquired from the football stadium. The performance of this model is validated and it proves the sturdiness of the system with an accuracy of 94.5 percentage in recognizing the violent action.