## 1 Introduction  
Video anomaly detection is a highly challenging task in unsupervised video analysis. In recent years, surveillance video anomaly detection has gained widespread attention owing to its applications in public security, social security management, and the rising trends in deep learning and computer vision. Inherently, the abnormal events are also complex in nature due to various reasons such as messy background/objects, motion in the scene, etc. Therefore, the complexity of the abnormal events creates a bottleneck issue in the detection of such events from real-world video data. Additionally, handling and modeling of video data itself are difficult because of its high dimensionality, noise, and a diversity of events and interactions involved. So far, many efforts have been reported in literatures that provide in-depth studies on video anomaly detection by mainly focusing on appearance features, depth features, optical flow modeling, etc., but very less attention has been paid to skeleton-based video anomaly detection models. Likewise, we explicitly use a common structure of surveillance video, i.e., people and objects moving on a static background where most abnormal phenomena are caused by the humans. However, most of these models are primarily based on the image level and instead of focusing on normal pattern modeling of humans, emphasize more on background hence increasing the burden on background model. Therefore, to mitigate the above stated issues, we employ skeleton features and take advantage of their compactness, strong structure, semantically rich properties, and strong description of human behavior and movement. In this way, the analysis can be free from any interference caused by the factors such as illumination and busy background. Nowadays, graph convolutional networks (GCN) are listed among the most popular methods available for analyzing Non-Euclidean Structure Data. As an effective representation of Non-Euclidean Structure Data, they can effectively capture spatial (intra-frame) and temporal (inter-frame) information. While referring to skeleton-based action recognition, [PERSON] et al. [1] proposed the spatial temporal graph convolutional networks (ST-GCN), which first apply GCN to model skeleton data. The ST-GCN model has been proven to perform well on skeleton data [2; 3; 4], but as spatiotemporal graph convolution operation only operates on a local neighborhood node and is restricted by the size of the convolution kernel, it lacks the global information. Moreover, the correlation between body joints in the human skeleton that are not directly connected are also underestimated, e.g., the left hand and right foot. Transformer self-attention [5] was originally applied in natural language processing tasks to encode the short-distance and long-distance correlations between the words in sentences. Likewise, considering the sequential nature and hierarchical structure of the human skeleton sequences, this mechanism can be extended to the skeleton data. Self-attention can resolve the major shortcoming of ST-GCN (i.e., it can only capture the local features of the spatial dimension) because of its flexibility in dealing with long dependencies. Recently, the self-attention method is used in one of the works to solve the locality of the convolution operator by capturing the global context of pixels in the image [6]. The proposed novel spatial temporal self-attention augmented graph convolutional network (SAA-Graph) contains a new graph convolution operator by combining improved spatial graph convolution operator with a modified transformer self-attention operator to capture both local and global information of the joints. The improved spatial graph convolution operator uses a data-driven approach to improve the flexibility of the model building graphs and brings in more versatility to align with various data samples. Our work uses self-attention mechanism on skeleton data to enhance the graph convolution.