Violence is a critical social problem and demands to evaluate through computer vision approaches. At present, the incidences of violent actions get grown in the community, particularly in public places due to several economic and social causes. Moreover, our society’s populations are increasing day by day and it is challenging to keep citizens within limits as well as monitoring human activities in crowd is too hard. Thus, government organizations including local bodies, require examining such occurrences through smart surveillance. In this research, a lightweight computational architecture has been presented to classify non-violent and violent activities. A model has been proposed to extract time-based features using smart devices, high-speed wireless networks and cloud servers to classify real-time human activities. For this purpose, a deep learning-based model is employed to detect violent activities and assist the stakeholders in exposing such activities in real-time. Convolutional long short-term memory (Conv-LSTM) is employed to extend fully connected LSTM (FC-LSTM) to capture the frame and detect violent actions. The proposed model accomplished 95.16% validation accuracy using a standard crowd anomaly dataset.

暴力是一个严重的社会问题，需要通过计算机视觉方法进行评估。目前，由于多种经济和社会原因，社区暴力行为的发生率有所增加，尤其是在公共场所。此外，我们社会的人口每天都在增加，将公民限制在一定范围内以及在人群中监控人类活动太难了。因此，包括地方机构在内的政府组织需要通过智能监控来检查此类事件。在这项研究中，提出了一种轻量级计算架构来对非暴力和暴力活动进行分类。已经提出了一种使用智能设备、高速无线网络和云服务器提取基于时间的特征的模型，以对实时人类活动进行分类。以此目的，采用基于深度学习的模型来检测暴力活动并帮助利益相关者实时揭露此类活动。卷积长短期记忆 (Conv-LSTM) 用于扩展全连接 LSTM (FC-LSTM) 以捕获帧并检测暴力动作。所提出的模型使用标准人群异常数据集实现了 95.16% 的验证准确率。