Name
Abstract | ||
|---|---|---|
We present a novel approach for automatic detection of social groups of pedestrians in crowds. Instead of computing pairwise similarity between pedestrian trajectories, followed by clustering of similar pedestrian trajectories into groups, we cluster pedestrians into a groups by considering only start source and stop sink locations of their trajectories. The paper presents the proposed approach and its evaluation using different datasets: experimental results demonstrate its effectiveness achieving significant accuracy both under dichotomous and trichotomous coding schemes. Experimental results also show that our approach is less computationally expensive than the current state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25903-1_22 | ACIVS |
Keywords | Field | DocType |
Group detection,Hierarchical clustering,Crowds analysis | Hierarchical clustering,Group detection,Social group,Pairwise comparison,Computer vision,Crowds,Pedestrian,Computer science,Coding (social sciences),Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
9386 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sultan Daud Khan | 1 | 13 | 3.78 |
| Giuseppe Vizzari | 2 | 652 | 100.25 |
| Stefania Bandini | 3 | 964 | 191.51 |
| Saleh M. Basalamah | 4 | 89 | 14.27 |