Name
Abstract | ||
|---|---|---|
The abnormal event detection becomes an important topic recently. This paper presents a method to detect the crowd gathering, as well as the commotion event after the crowd gathering. The proposed stillness model and the motion model are based on the improved background subtraction and the optical flow feature. We construct the long-term stillness level by the break bucket model and clustering the instantaneous stillness level. Then the crowd gathering event is decided by the threshold with the long-term stillness level. Furthermore, the motion model is applied for detecting the commotion event after the crowd gathering. In the experiment, we used the dataset of PET2009. The proposed method is verified by the experiment with 97% accuracy. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-020-08827-4 | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Crowd abnormal behaviors,Surveillance application,Crowd feature engineering,Image recognition | Journal | 79.0 |
Issue | ISSN | Citations |
27-28 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deng-Shun Yang | 1 | 0 | 0.34 |
| Chun-Yu Liu | 2 | 0 | 0.34 |
| Wei-Hao Liao | 3 | 0 | 0.34 |
| Shanq-Jang Ruan | 4 | 375 | 55.44 |