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Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM

机译：包含大数据分析和双向LSTM深度学习的足球场实时暴力检测框架

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摘要

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. (C) 2019 Elsevier B.V. All rights reserved.
机译：足球是世界上最受欢迎的运动，全世界有40亿球迷。据报道，比赛期间或比赛后的暴力发生率很高。在体育场观看或玩游戏的人或球员进行的暴力或破坏性行为被称为足球流氓行为。为了防止或控制暴力，仅需要实时暴力检测系统来监视人群和玩家的行为，以便在暴力即将发生之前采取必要的措施。甚至对于系统来说，也有必要找出游戏中攻击是无意还是有意。本文提出了一种实时暴力检测系统，该系统可处理大量输入流数据并通过人类智能仿真识别暴力。系统的输入是来自不同源的大量实时视频流，这些流在Spark框架中进行处理。在Spark框架中，使用HOG（定向直方图）功能分离帧并提取单个帧的特征。然后基于暴力模型，人体部位模型和否定模型等特征对帧进行标记，这些特征用于训练双向长短期记忆（BDLSTM）网络以识别暴力场景。双向LSTM可以正向和反向访问信息。因此，根据过去和将来的信息生成了输出。该网络使用暴力互动数据集（VID）进行了培训，其中包含2314个视频，其中有1077架战斗视频和1237架非战斗视频。此外，为了使模型对暴力检测具有鲁棒性，我们创建了一个数据集，该数据集包含从足球场获取的410个具有非暴力场景的视频片段和409个具有暴力场景的视频片段。验证了该模型的性能，证明了系统的坚固性，识别暴力行为的准确度为94.5％。 （C）2019 Elsevier B.V.保留所有权利。

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