图结构多尺度变换的视频异常检测

目的 在监控场景的视频异常检测中，存在数据量大和检测速度慢的问题，为此提出图结构多尺度变换下的视频异常检测方法。方法 针对视频中光流特征的空间结构存在关联性，提出构建光流特征网络图结构，并在相关约束下利用光流特征图结构的迭代尺度化变换，有效降低视频异常检测中的光流特征数量，从而完成特征优化。光流特征图结构的尺度化变换首先利用光流特征图结构的图拉普拉斯矩阵所对应的最大特征向量的极性来筛选顶点，完成图的下采样操作；接着利用Kron规约构建顶点间的内在连接，重新构建光流特征图结构。结果 该方法能够提高视频异常检测算法的检测速度，但这是在略微降低检测精度的前提下实现的。在UMN数据集中，当尺度化图结构仅一次时的检测精度下降了3.2%，但检测速度提升了19.1%。这对整个视频集的检测速度的提升有明显效果。当尺度化次数为两次时的检测精度下降了7.3%，但这时检测效果达不到实际要求。此时，当尺度化图结构仅一次时异常检测的效果能达到预期。在Web数据集中，当尺度化图结构仅一次时，检测精度下降了1.9%，但检测速度提升了32%；尺度化两次时，检测精度降低了4.8%，检测速度提升了51%。因此，需要根据检测精度与检测速度的综合考虑后，选择尺度化次数是一次还是两次。但是随着尺度化次数的提高，这时检测效果就不能符合要求。结论 本文利用不规则的网络图结构来更好地表述特征之间的空间关系，并且多尺度变换后图结构也能表述特征间仍然保留有较强的空间关系。在不同的视频监控场景下，根据对检测精度与检测速度的综合考虑后选择合适的尺度化次数，从而实现快速异常检测。

Video anomaly detection of multiscale transformation of graph structure

Objective 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.Method 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.Result 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.Conclusion 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.