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.

In order to solve the problems of large amount of network computation and difficulty in distinguishing falling-like behaviors in fall detection algorithms， a fall detection algorithm based on joint point features was proposed. Firstly， based on the current advanced CenterNet algorithm， a Depthwise Separable Convolution-CenterNet （DSC-CenterNet） joint point detection algorithm was proposed to accurately detect human joint points and obtain joint point coordinates while reducing the amount of backbone network computation. Then， based on the joint point coordinates and prior knowledge of the human body， the spatial and temporal features expressing the fall behavior were extracted as the joint point features. Finally， the joint point feature vector was input into the fully connected layer and processed by Sigmoid classifier to output two categories： fall or non-fall， thereby achieving the fall detection of human targets. Experimental results on UR Fall Detection dataset show that the proposed algorithm has the average accuracy of fall detection under different state changes reached 98.00%， the accuracy of distinguishing falling-like behaviors reached 98.22% and the fall detection speed of 18.6 frame/s. Compared with the algorithm of the original CenterNet combining with joint point features， the algorithm of DSC-CenterNet combining with joint point features has the average detection accuracy increased by 22.37%. The improved speed can effectively meet the realtime requirement of the human fall detection tasks under surveillance video. This algorithm can effectively increase fall detection speed and accurately detect the fall state of human body， which further verifies the feasibility and efficiency of fall detection algorithm based on joint point features in the video fall behavior analysis.