Hongmin Zhang, Dingding Yan, Qianqian Tian. Improved spatio-temporal graph convolutional networks for video anomaly detection[J]. Opto-Electronic Engineering, 2024, 51(5): 240034
- Opto-Electronic Engineering
- Vol. 51, Issue 5, 240034 (2024)
Fig. 1. Improved spatio-temporal graph convolutional network model framework
Fig. 2. Comparison between GCN module and CRF-GCN module. (a) GCN module; (b) CRF-GCN module
Fig. 3. Flowchart of mean-field inference for CRF-GCN
Fig. 4. Test results of UCSD Ped2 dataset. (a) Test003; (b) Test012
Fig. 5. Test results of ShanghaiTech dataset. (a) 04_0004; (b) 12_0173
Fig. 6. Test results of IITB-Corridor dataset. (a) Test000228; (b) Train000139 (Normal)
Fig. 7. Noised experiments. (a) AUC loss for training with noise-added data; (b) ACC loss for training with noise-added
|
Table 1. UCSD Ped2, ShanghaiTech and IITB-Corridor datasets
|
Table 2. Comparison results of different methods on UCSD Ped2 dataset
|
Table 3. Comparison results of different methods on ShanghaiTech dataset
|
Table 4. Comparison results of different methods on IITB-Corridor dataset
|
Table 5. Comparison results of different methods on complexity
|
Table 6. Results of ablation experiments
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20