# A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras
###### Abstract  
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. a 202213 January 202213 January 20213 January 20  
considered abnormal, while it acts normal in a shooting club. On the other hand, some behaviour is not intrinsically abnormal, but it would be an anomaly in a specific location and condition called local abnormal event (LAE) [5]. Besides, [PERSON] in [6] proposed abnormal events as \"an action done at an unusual location, at an unusual time\". From a learning standpoint, anomaly detection can be divided into three approaches: supervised, unsupervised, and semi-supervised, as a significant and well-known categorizing for learning methods. In supervised learning, there are two different approaches by considering whether the model is trained by only one category or all existing categories [7]. In other words, in single model learning, the model is trained by only normal(or abnormal) events, whereas in multi-model learning, both normal and abnormal events need to be trained. In the single model learning, anomalous events distinguished from normal ones by learning a threshold for normality definition [8; 9; 10], learning of a multidimensional model of normal events within the feature space [11; 12; 13; 14; 15; 16; 17] and learning rules for model definition [18]. While, for the multi-model learning approach, which is particularly used when there are several groups of anomalies, each class will be trained dependently or independently [7]. On the other hand, an anomaly detection problem is generally considered as an unsupervised learning problem [19]. This technique deal with unlabeled data in which it is assumed that Normal events frequently occur while Abnormal events rarely happen in data. Considering all rare events as anomalous is one of the drawbacks of this learning [7]. Several clustering algorithms in unsupervised learning consider normal and abnormal events should be well separated in the feature space [20; 21; 22]. Besides, the semi-supervised Anomaly detection approach neither is too reliable on labeled data like the supervised approach nor have a low accuracy as unsupervised models [23]. This model tries to diminish the differences between supervised and unsupervised techniques [19].