Name
Abstract | ||
|---|---|---|
Collective motion stems from the coordinated behaviors among individuals of crowds, and has attracted growing interest from the physics and computer vision communities. Collectiveness is a metric of the degree to which the state of crowd motion is ordered or synchronized. In this letter, we present a scheme to measure collectiveness via link prediction. Toward this aim, we propose a similarity index called superposed random walk with restarts (SRWR) and construct a novel collectiveness descriptor using the SRWR index and the Laplacian spectrum of a network. Experiments show that our approach gives promising results in real-world crowd scenes, and performs better than the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1587/transinf.2015EDL8011 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
collectiveness, link prediction, random walk | Computer vision,Random walk,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
E98D | 8 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Jiang | 1 | 2 | 6.12 |
| Di Wu | 2 | 0 | 0.68 |
| Qizhi Teng | 3 | 0 | 0.34 |
| Xiaohai He | 4 | 135 | 25.57 |
| Mingliang Gao | 5 | 28 | 7.05 |