In recent years， many studies that use human skeleton graph for video anomaly detection only consider the directly connected nodes when describing the strength of human skeleton connection， focusing on a small moving region and ignoring local features， so it is still very difficult to accurately detect pedestrian abnormal events. To solve the above problems， a video pedestrian anomaly detection method called PAD-SGMA （video Pedestrian Anomaly Detection method based on Skeleton Graph and Mixed Attention） was proposed. The association between skeleton points was extended， the root node was connected with the nodes that were not directly connected， and the human skeleton graph was divided to obtain the local features of the human skeleton. In the graph convolution module， static global skeleton， local region skeleton and attention-based adjacency matrix were used to capture the hierarchical representation. Secondly， a new convolutional network of spatio-temporal channel mixed attention was proposed， in which a mixed attention module was added to focus on spatial and channel relationships， to help the model enhance distinguishing features and pay different degrees of attention to different joints. In order to verify the proposed model， experiments were carried out on a large-scale open standard dataset ShanghaiTech Campus dataset， and the experimental results showed that the AUC（Area Under Curve） of PAD-SGMA was increased by 0.018 compared with GEPC （Graph Embedded Pose Clustering）.

Constructing the transition probability matrix for outlier detection by using traditional graph-based methods requires the use of the overall distribution of the data， and the local information of the data is easily ignored， resulting in the problem of low detection accuracy， and using the local information of the data may lead to “suspended link” problem. Aiming at these problems， an Outlier Detection algorithm based on Hologram Stationary Distribution Factor （HSDFOD） was proposed. Firstly， a local information graph was constructed by adaptively obtaining the set of neighbors of each data point through the similarity matrix. Then， a global information graph was constructed by the minimum spanning tree. Finally， the local information graph and the global information graph were integrated into a hologram to construct a transition probability matrix for Markov random walk， and the outliers were detected through the generated stationary distribution. On the synthetic datasets A1 to A4， HDFSOD has higher precision than SOD （Outlier Detection in axis-parallel Subspaces of high dimensional data）， SUOD （accelerating large-Scale Unsupervised heterogeneous Outlier Detection）， IForest （Isolation Forest） and HBOS （Histogram-Based Outlier Score）； and AUC （Area Under Curve） also better than the four comparison algorithms generally. On the real datasets， the precision of HSDFOD is higher than 80%， and the AUC of HSDFOD is higher than those of SOD， SUOD， IForest and HBOS. It can be seen that the proposed algorithm has a good application prospect in outlier detection.