For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information. To solve this, we propose a novel weakly supervised adaptive graph convolutional network (WAGCN) to model the complex contextual relationship among video segments. By which, we fully consider the influence of other video segments on the current one when generating the anomaly probability score for each segment. Firstly, we combine the temporal consistency as well as feature similarity of video segments to construct a global graph, which makes full use of the association information among spatial-temporal features of anomalous events in videos. Secondly, we propose a graph learning layer in order to break the limitation of setting topology manually, which can extract graph adjacency matrix based on data adaptively and effectively. Extensive experiments on two public datasets (i.e., UCF-Crime dataset and ShanghaiTech dataset) demonstrate the effectiveness of our approach which achieves state-of-the-art performance.

翻译：对于监测不力的异常点检测,大多数现有工作仅限于由于无法模拟长期背景信息而导致的视频代表性不足的问题。为了解决这个问题,我们提议建立一个新颖的、监督不力的适应图变动网络(WAGCN),以模拟视频段之间的复杂背景关系。我们据此充分考虑到其他视频段在产生每个段的异常概率分数时对当前视频段的影响。首先,我们将时间一致性和视频段的特征结合起来,以构建一个全球图,充分利用视频中异常事件空间时空特征之间的关联信息。第二,我们提出一个图表学习层,以打破设置地形学的局限性,从而能够根据适应性和有效性的数据提取图表相近矩阵。关于两个公共数据集(即UCF-犯罪数据集和上海科技数据集)的广泛实验表明我们实现最新性表现的方法的有效性。