Surveillance cameras have been increasingly used in many public and private spaces in recent years to increase the security of those areas. Although many companies still recruit someone to monitor the cameras, the person recruited is more likely to miss some abnormal events in the camera feeds due to human error. Therefore, monitoring surveillance cameras could be a waste of time and energy. On the other hand, many researchers worked on surveillance data and proposed several methods to detect abnormal events automatically. As a result, if any anomalous happens in front of the surveillance cameras, it can be detected immediately. Therefore, we introduced a model for detecting abnormal events in the surveillance camera feed. In this work, we designed a model by implementing a well-known convolutional neural network (ResNet50) for extracting essential features of each frame of our input stream followed by a particular schema of recurrent neural networks (ConvLSTM) for detecting abnormal events in our time-series dataset. Furthermore, in contrast with previous works, which mainly focused on hand-crafted datasets, our dataset took real-time surveillance camera feeds with different subjects and environments. In addition, we classify normal and abnormal events and show the method’s ability to find the right category for each anomaly. Therefore, we categorized our data into three main and essential categories: the first groups mainly need firefighting service, while the second and third categories are about thefts and violent behaviour. We implemented the proposed method on the UCF-Crime dataset and achieved 81.71% in AUC, higher than other models like C3D on the same dataset. Our future work focuses on adding an attention layer to the existing model to detect more abnormal events.

Violent behaviour is always an important issue that threatens any society. Therefore, many organizations have used surveillance cameras to monitor such events to preserve public safety and mitigate potential harm. It is difficult for human operators to monitor the copious camera feed manually, however, automated systems are employed to enhance the accuracy of violence detection and reduce errors. In this paper, we propose a novel model named KianNet that effectively detects violent incidents inside recorded events by combining ResNet50 and ConvLSTM architectures with a multi-head self-attention layer. The utilization of ResNet50 enables robust feature extraction, while ConvLSTM makes it easier to take advantage of the temporal dependencies in the video sequences. Furthermore, the multi-head self-attention layer enhances the model’s ability to focus on relevant spatiotemporal regions and their discriminatory capacity. Empirical investigations on large datasets UCF-Crime and RWF confirm that the proposed model outperforms its competitors.