基于时空依赖关系和特征融合的弱监督视频异常检测

Weakly Supervised Video Anomaly Detection Based on Spatio-Temporal Dependence and Feature Fusion

弱监督视频异常检测由于抗干扰性强、数据标注要求低,成为视频异常事件检测研究的热点。在现有的工作中,大多数弱监督视频异常检测方法认为各个视频段独立同分布,单独判断每个视频段是否异常,忽略了视频段间的时空依赖关系。为此,提出了一种基于时空依赖关系和特征融合的弱监督视频异常检测方法,在保留视频段原始特征的同时,使用视频段之间的索引距离和特征相似程度拟合视频段的时间和空间依赖关系,构建视频段的关系特征。通过融合原始特征和关系特征,更好地表达视频的动态特性和时序关系。在UCF-Crime和ShanghaiTech两个基准数据集上进行了大量实验,实验结果表明所提方法的AUC指标优于其他方法,AUC值分别达到了80.1%和94.6%。

Weakly supervised video anomaly detection has become a hot spot in video anomaly detection research due to its strong anti-interference and low data labeling requirements.In the existing methods,most of the weakly supervised video anomaly detection methods assume that the clips in each video distribute independently,and determine whether it is abnormal for each video clip independently,ignoring the temporal and spatial information between video clips.To alleviate these problems,this paper proposes a weakly supervised anomaly detection method based on spatio-temporal dependence and feature fusion.Retaining the original characteristics of video clips,this method uses the distance of index and the similarity of features between video clips to fit the time dependence and the spatial dependencies of video,which builds the relationship characteristics of video clips.By fusing the original features and relationship features,the dynamic characteristics and temporal relationship of videos can be better expressed.Extensive experiments on two benchmark datasets,UCF-Crime and ShanghaiTech,demonstrate that the proposed method outperforms other methods with the AUC values reaching 80.1%and 94.6%,respectively.