The further expansion of the current video surveillance market has provided video surveillance is showing with a large amount of data

which are difficult to store and process.In video anomaly detection of the monitoring scene

identifying the abnormal events rapidly and accurately is particularly important.In this study

the optical flow features extracted from the video are taken as examples.On the basis of the association of the space structure of the optical flow features

the relationship of the optical flow features is preserved after multiscale transformation of the graph structure.Our method can achieve the purpose of rapid anomaly detection by reducing the number of optical flow features. Therefore

the video anomaly detection method based on multiscale transformation of the graph structure is proposed. Aiming at the relevance of the spatial structure of the optical flow features in the video

constructing the network graph structure of the optical flow features is proposed.Under the relevant constraints

the iterative scale transformation of the graph structure of the optical flow is used to reduce the number of optical flow features effectively to complete feature optimization in video anomaly detection.The process of scale transformation of the graph structure is described as follows.First

we use the polarity of the largest eigenvector of the Laplacian matrix of the graph structure of the optical flow features to filter the vertex and complete the graph downsampling.Then

we use Kron reduction to construct the inner connection between the vertices and reconstruct the graph structure of the optical flow features.Therefore

after the multiscale transformation of the graph structure

we can generate a graph structure of the optical flow features with a small number of vertices that are closely related to the spatial features.Thus

the optimization of the optical flow features can be achieved and the subsequent anomaly detection can become rapid and efficient.In video monitoring

the multiscale transformation of the graph structure helps store and process the feature data of the current video monitoring. Experimental results show that this method can improve the detection speed of the video anomaly detection algorithm only when the detection accuracy is slightly decreased.In the UMN dataset

when the scale number of the graph structure is only one

the detection accuracy is reduced by 3.2% but the detection speed is improved by 19.1%.Thus

the detection speed of the entire video set is significantly affected.When the scale number is two

the detection accuracy is decreased by 7.3% and the results cannot meet the actual requirements.Thus

the effect of anomaly detection can be achieved when the scale number is only one.In the Web dataset

when the scale number of the graph structure is only one

the detection accuracy is reduced by 1.9% but the detection speed is increased by 32%.When the scale number is two

the detection accuracy is reduced by 4.8% but the detection speed is improved by 51%.Therefore

on the basis of the detection accuracy and detection speed

we select the scale numbers of one or two.However

with the increase in the scale number

the detection effect cannot meet the requirements.From the two different experiments conducted to verify the method proposed in this study

we can conclude that the multiscale transformation of the graph structure exhibits a good performance in video anomaly detection.When the detection accuracy is slightly reduced

the multiscale transformation of the graph structure can obviously improve the detection speed of video anomaly detection. In this study

we use the irregular network graph structure to fully describe the spatial relationship between features.After the multiscale transformation of the graph structure

we can maintain a strong spatial relationship between the features.In different video surveillance scenes

we select the appropriate scale number in accordance with the detection accuracy and detection speed to achieve rapid anomaly detection.