Authors:
J. Rosell-Ortega
;
G. Andreu-Garćıa
;
A. Rodas-Jordà
;
V. Atienza-Vanacloig
and
J. Valiente-González
Affiliation:
DISCA, Universidad Polite´cnica de Valencia, Spain
Keyword(s):
Surveillance, object classification, object recognition, shadow removal, feature sets.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Tracking of People and Surveillance
Abstract:
We study two different sets of features with the aim of classifying objects from videos taken in an airport. Objects are classified into three different classes: single person, group of people, and luggage. We have used two different feature sets, one set based on classical geometric features, and another based on average density of foreground pictures in areas of the blobs. In both cases, easily computed features were selected because our system must run under real-time constraints. During the development of the algorithms, we also studied if shadows affect the classification rate of objects. We achieved this by applying two shadow removal algorithms to estimate the usefulness of such techniques under real-time constraints.