Name
Abstract | ||
|---|---|---|
AbstractAbnormal event detection has attracted great research attention in video surveillance. In this paper, the authors presented a robust method of trajectories clustering for abnormal event detection. This method is based on two layers and benefits from two well-known clustering algorithms: the agglomerative hierarchical clustering and the k-means clustering. Facing to the challenges related to the trajectories, e.g., different sizes, the authors introduce a preprocessing step to unify their sizes and reduce their dimensionality. The experimental results show the performance and accuracy of their proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4018/IJSI.2017100101 | Periodicals |
Keywords | DocType | Volume |
Abnormal Event, Clustering, Preprocessing, Trajectories, Video Surveillance | Journal | 5 |
Issue | ISSN | Citations |
4 | 2166-7160 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emna Fendri | 1 | 12 | 7.28 |
| Najla Bouarada Ghrab | 2 | 0 | 0.34 |
| Mohamed Hammami | 3 | 181 | 30.54 |