Name
Title | ||
|---|---|---|
Normal graph: Spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2019.12.148 | Neurocomputing |
Keywords | DocType | Volume |
Graph convolutional networks,Anomaly detection,Skeleton | Journal | 444 |
ISSN | Citations | PageRank |
0925-2312 | 2 | 0.37 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weixin Luo | 1 | 92 | 8.23 |
| Wen Liu | 2 | 49 | 3.57 |
| Shenghua Gao | 3 | 1607 | 66.89 |