Name
Abstract | ||
|---|---|---|
Abnormal event detection, as a hot research field in intelligent video monitoring system, has attracted many researchers' attention in recent years. In order to overcome the shortcomings of the semi-supervised model, namely the training sample is difficult to contain all possible situations, leading to the occurrence of error detection, we propose a method based on support vector data description (SVDD). The principle of the method is to train the model with normal data and abnormal data respectively to obtain two SVDD models, and then judge whether there are abnormal events according to the results of the two models. This method has been tested by existing data sets and achieved good results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IDAACS.2019.8924464 | 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) |
Keywords | Field | DocType |
abnormal event detection,intelligent video monitoring system,support vector data description,semi-supervised model | Data set,Monitoring system,Computer science,Support vector machine,Error detection and correction,Artificial intelligence,Machine learning,Data description | Conference |
Volume | ISBN | Citations |
1 | 978-1-7281-4070-4 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinlu Zong | 1 | 0 | 0.34 |
| Lu Zhang | 2 | 0 | 1.01 |
| Jiayuan Du | 3 | 0 | 0.34 |
| Wei Liu | 4 | 23 | 3.19 |
| Qian Huang | 5 | 0 | 0.34 |