Name
Abstract | ||
|---|---|---|
Security surveillance of public scene is closely relevant to routine safety of individual. Under the stimulus of this concern, abnormal event detection is becoming one of the most important tasks in computer vision and video processing. In this paper, we propose a new algorithm to address the visual abnormal detection problem. Our algorithm decouples the problem into a feature descriptor extraction process, followed by an AutoEncoder based network called cascade deep AutoEncoder (CDA). The movement information is represented by a novel descriptor capturing the multi-frame optical flow information. And then, the feature descriptor of the normal samples is fed into the CDA network for training. Finally, the abnormal samples are distinguished by the reconstruction error of the CDA in the testing procedure. We validate the proposed method on several video surveillance datasets. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11704-018-7407-3 | Frontiers of Computer Science |
Keywords | Field | DocType |
abnormal event detection, multi-frame optical flow, cascade deep autoencoder | Video processing,Feature descriptor,Autoencoder,Pattern recognition,Computer science,Reconstruction error,Cascade,Artificial intelligence,Optical flow | Journal |
Volume | Issue | ISSN |
14 | 2 | 2095-2236 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tian Wang | 1 | 21 | 6.47 |
| Meina Qiao | 2 | 3 | 0.73 |
| Aichun Zhu | 3 | 16 | 8.10 |
| Guangcun Shan | 4 | 2 | 2.44 |
| Hichem Snoussi | 5 | 509 | 62.19 |