Name
Abstract | ||
|---|---|---|
In this paper, a study on effective exploitation of geometrical features for classifying surveillance objects into a set of pre-defined semantic categories is presented. The geometrical features correspond to object's motion, spatial location and velocity. The extraction of these features is based on object's trajectory corresponding to object's temporal evolution. These geometrical features are used to build a behaviour-based classifier to assign semantic categories to the individual blobs extracted from surveillance videos. The proposed classification framework has been evaluated against conventional object classifiers based on visual features extracted from semantic categories defined on AVSS 2007 surveillance dataset. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-28033-7_10 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Object classification,geometrical models,surveillance videos,object tracking,motion features | Computer vision,Pattern recognition,Computer science,Video tracking,Artificial intelligence,Classifier (linguistics),Trajectory | Conference |
Volume | ISSN | Citations |
255 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Virginia Fernandez Arguedas | 1 | 27 | 4.20 |
| Krishna Chandramouli | 2 | 62 | 11.07 |
| ebroul izquierdo | 3 | 1050 | 148.03 |