## 2 Related Work  
### Video Anomaly Detection  
Video anomaly detection is defined as a way to find abnormal patterns or actions in the data. These abnormalities are defined as infrequent or rare events. Traditional methods for abnormal event detection that extract and analyze the hand-crafted low-level visual features are unable to characterize the more complex behaviors. Additionally, the extracted features by such methods are relatively single, which demonstrates the fact that the generalization ability of hand-crafted features is usually weak and is not robust to crowd scenes. For instance, trajectory [7; 8] is used to describe the trajectory of moving objects. Similarly, Histogram of Oriented Gradient (HOG) [9] and Histogram of Flow (HOF) [10] can characterize the shape and contour information of the human body in a static image. Accordingly, optical flow [11] can describe the changes in the gray value of pixels between adjacent frames and is often used to characterize the motion information. [PERSON] et al. [12] associated optical flows to capture short-term trajectories between multiple frames and described short-term trajectories by histogram-based shape descriptor. However, the mentioned methods revealed only a suboptimal performance when subjected to complex surveillance scenarios and large-scale video anomaly detection datasets.  
Recently, various works have used deep learning-based models to address the problem of video anomaly detection. Such models can be roughly categorized into reconstructive models, predictive models, and generative models. The reconstruction model uses the difference between reconstructed image and the original image as a basis for scoring and positioning of anomaly detection, and often relies on autoencoders [13; 14]. The prediction model, on the other hand, utilizes the recurrent neural networks [15; 16; 17] or 3D convolutions [14] and emphasizes on addition of prediction and generation of future frames based on the original reconstruction to calculate the loss. Finally, the generative models primarily use the variational autoencoders (VAEs) or GANs to reconstruct, predictor model the distribution of the data. The early anomaly detection work of [PERSON] et al. [18] has been used for human activity recognition in wide-area automatic visual surveillance. A method proposed by [PERSON] et al. [19] uses a future frame prediction model by combining U-Net and Beyond-MSE. [PERSON] et al. [20] proposed a Fast Sparse Coding Network based on High-level Features to discriminate spatio-temporal fusion features for video anomaly detection to achieve higher accuracy. In another work, [PERSON] et al. [21] adhered two RNN branches together to form global and local features, using a message-passing encoder-decoder RNN architecture. The work by [PERSON] et al. [22] is the first one which applies graph convolutional networks on skeleton-based video anomaly detection to analyze the graph connection of human joints. Progressively, [PERSON] et al. [23] proposed an approach to use embedded  
Figure 1: Framework Diagram: First, we perform pose estimation algorithm to extract skeletons for each frame in each video. The extracted pose of skeleton is encoded to generate a latent vector using the encoder part of a spatial temporal self-attention augmented graph convolutional autoencoder. The deep embedded clustering layer is used to softly assign the latent vector to the clusters, and \(P_{nk}\) represents the probability of the sample being assigned to the cluster \(k\).  
pose graphs and a Dirichlet process mixture for video anomaly detection with a new coarse-grained setting for exploring broader aspects of video anomaly detection.