Normal graph: Spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection
Normal graph: Spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection
复制标题
普通图：基于时空图卷积网络的预测网络，用于基于骨架的视频异常检测
DOI：
10.1016/j.neucom.2019.12.148
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发表时间：
2020
期刊：
影响因子：
6
通讯作者：
Shenghua Gao
中科院分区：
文献类型：
--
作者：
Weixin Luo;Wen Liu;Shenghua Gao
针对基于骨骼的视频异常检测，本文重点分析了人体关节的图连通性，该方法比基于图像级重建或基于预测的方法更有效和高效，这些方法可能受到复杂背景的影响。具体来说，我们提出了一种基于时空图卷积网络的基于骨架的视频异常检测预测网络。换句话说，我们建立一个法线图来描述正常数据中节点的图连接，其中异常事件的节点将是该图的离群值。据我们所知，这是第一次将图卷积网络应用于基于骨架的视频异常检测。实验表明，与那些基于图像级重建或基于预测的方法以及基于关节的RNN方法相比，我们提出的正态图达到了最先进的性能。
This paper focus on analyzing graph connection of human joints for skeleton based video anomaly detection, which is more effective and efficient than those image-level reconstruction based or prediction based methods that may be affected by complex background. Specifically, we propose a spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection. In other words, we build a normal graph describing graph connection of joints in normal data, where joints of abnormal events will be outliers of this graph. To our knowledge, this is the first work to apply graph convolutional networks on skeleton-based video anomaly detection. Experiments show that our proposed normal graph achieves the-state-of-art performance, compared to those image-level reconstruction-based or prediction-based methods, as well as RNN based methods upon joints.