Name
Abstract | ||
|---|---|---|
This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based in the hypothesis that, when people try to reach a destination, they use the path that takes less time, taking into account environmental information like the type of terrain or what other people did before. Thus, a minimal path approach can be used to model human trajectory behaviour. We develop a modified Fast Marching Method that allows us to include both velocity and orientation in the Front Propagation Approach, without increasing its computational complexity. Combining all the information, we create a time surface that shows the time a target need to reach any given position in the scene. We also create different metrics in order to compare the time surface against the real behaviour. Experimental results over a public dataset prove the initial hypothesis' correctness. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CVPR.2014.327 | CVPR |
Keywords | Field | DocType |
behavioural sciences computing,computational complexity,pedestrians,unsupervised learning,video surveillance,computational complexity,front propagation approach,human trajectory behaviour model,minimal path approach,modified fast marching method,temporal information,unsupervised pedestrian trajectory modelling,geodesic active contours,pedestrian behavior,trajectory analysis | Front propagation,Computer vision,Pedestrian,Computer science,Fast marching method,Terrain,Correctness,Artificial intelligence,Trajectory analysis,Trajectory,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
1063-6919 | 8 | 0.50 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brais Cancela | 1 | 66 | 9.19 |
| A. Iglesias | 2 | 8 | 0.50 |
| M Ortega | 3 | 235 | 37.13 |
| Manuel G. Penedo | 4 | 185 | 35.91 |