## 2 Related Works  
In this section, we present a brief review of some recent anomalous event detection algorithms. Previously, some particular task-specific anomalous event detectors, including an abandoned object detector [24], traffic monitoring systems [25; 26; 27], and crowd violence detector [28], have been proposed. The generalization of these detectors is challenging. Several studies [5; 6; 7; 8; 9; 29] have used traditional approaches to solve these challenges, such as the sparse coding technique [5; 8], or the dictionary learning technique [6; 7]. These techniques considered the anomalous event detection task as an outlier-detection task. They train these models only with normal event videos, and these models detect abnormality if they detect outliers from the learned pattern of the model, leading these models vulnerable to false alarms. As previously mentioned, proper feature extraction is key to an effective anomalous event detection method. Previously, hand-crafted feature extraction methods [30; 31] have been proposed in this task, which utilizes trajectory features at a low-level. Low-level trajectory features represent the regular pattern as a sequence of image coordinates. Since trajectory features are mainly conditioned on the object tracking concept, these features are not robust in case of crowded and complex videos with multiple shadows and occlusions. Thus, these methods [30; 31] quickly fail in such cases. To overcome these shortcomings, low-level spatial-temporal feature extraction methods, such as histogram of oriented flows (HOF) [32] and histogram of oriented gradients (HOG) [33], have been proposed. Later, [PERSON] et al. [34] modeled normal patterns with spatial-temporal features by exploiting the Markov random field (MRF). In the study [35], the local optical flow pattern is modeled using a mixture of probabilistic PCA (MPPCA). However, in another study [5], local histograms of optical flow are modeled using an exponential distribution. [PERSON] et al. [7] proposed the Mixture of Dynamic Textures (MDT). However, to extract useful features from the videos, a 3D ResNet [21] is utilized in our study, as inspired by related studies [21; 22]. A breakthrough of deep learning techniques in the field of computer vision tasks, such as in image processing [22], action recognition [15; 18; 21; 23; 24; 25; 36; 37], object detection [38], object tracking [39; 40], and re-identification [41; 42] tasks, inspired the researchers to explore these techniques in the anomalous event detection task. Consequently, some recent studies [1; 2; 3; 4; 10; 12; 13; 43; 44; 45] were proposed mostly using auto-encoder methods [1; 3; 12; 13] and recurrent neural networks (RNNs) [3; 4]. Author [PERSON] et al. [12] demonstrated the efficacy of deep learning methods by designing a multi-layer auto-encoder. To model normal activities, a 3D convolutional auto-encoder (Conv-AE) is proposed by [PERSON] et al. [13]. Furthermore, convolutional neural networks (CNNs) extract useful spatial features, whereas (RNNs) are generally used for modeling sequential or time-dependent data using its variants of long short-term memory (LSTM). Consequently, in some studies [3; 17], a convolutional LSTMs auto-encoder (ConvLSTM-AE) is proposed in which appearance and motion features are extracted simultaneously by leveraging both CNNs and RNNs, which further advances the performance of the Conv-AE based method [13]. In the study of [PERSON] et al. [5], a stacked RNN framework is proposed by utilizing a temporally coherent sparse coding based technique. Furthermore, in another study [16], the authors proposed the combined study of detection and recounting of anomalous events. Moreover, in the task of anomalous event detection, an increase in several event patterns results in a hike of complexity and uncertainty of feature distributions. Therefore, it is challenging to construct a well-generalized model that can be strictly-discriminative for normal events and anomalous events simultaneously.