## 1 Introduction  
Abnormal event detection and localization is a challenging and exciting task in video monitoring. Indeed, the security context in recent years has led to the proliferation of surveillance cameras, which generate large amounts of data. This flow of CCTV images creates a lack of efficiency of human operators. Moreover, studies show that they can miss up to 60% of the target events when they monitor nine or more displays [1]. In addition, after only 20 min of focus, the attention of most human operators decreases to well below acceptable levels [2]. This can lead to potential security breaches, especially when monitoring crowded scene videos. A possible solution to this problem is the development of automated video surveillance systems that can learn the normal behavior of a scene and detect any deviant event that may pose a security risk. Abnormal event detection, also known as anomaly detection, can be defined as a spatial temporal recognition problem, taking into account that the event to be recognized is not present in the training phase. In the context of video surveillance systems, anomalies are formed by rare shapes, motions or their combinations. The main challenge in abnormal event detection is extracting robust descriptors and defining classification algorithms adapted to detect suspicious behaviors with the minimum values of false alarms, while ensuring a good rate of detection. The initial studies in abnormal event detection focused on trajectory analysis [3; 4; 5], where a moving object is considered as abnormal if its trajectory doesn't respect the fitted model during the training period. The main limits of such method are the sensitivity to occlusion and the effectiveness of detecting abnormal shapes with normal trajectories. These methods can be used in detecting deviant trajectories in scenes containing few objects but not achieve satisfactory performance for other applications. Other methods such as low level local visual features [6; 7; 8] tried to overcome the limits of trajectory analysis and construct models by using handcrafted feature extractors. Among these methods, low local features such as histogram of oriented gradient (HOG), and histogram of optical flow (HOF) have been used to model the background and to construct the template behavior. However, these methods are usually specific to a given application and are not optimal for complex events. Besides, they don't link between local patterns, since local activity patterns of pixels is not efficient for behavior understanding. More complex methods used the concept of Bag of Video words (BOV) by extracting local video volumes obtained either by dense sampling or by selecting points of interest to construct the template behavior. However, the relationship between video volumes is often not taken into account. Derivatives of these methods [9] attempted to enhance the previous models by using not only the local region, but also the link between these regions for the overall understanding of the events. Usually the complexity of these methods makes them inefficient and time consuming for detection of abnormalities in crowded scenes. Nowadays, deep learning has aroused the interest of the scientific community and works have been carried out in several fields including agriculture [10], biology [11] and economics [12]. More specifically, deep learning based methods have demonstrated a high capacity on image processing [13; 14], which led to the use of supervised methods for the anomaly detection. These methods are generally based on the use of convolutional neural networks (CNNs) [15]. The main drawback of the supervised methods is the use of both normal and abnormal examples in the training phase which makes them not usable in real-world application for video surveillance, because it is very difficult to identify and reproduce all the abnormal events. Other deep learning based works [16; 17; 18] have achieved good performance on anomaly detection datasets.