2012 | OriginalPaper | Buchkapitel
Feature Selection by Relevance Analysis for Abandoned Object Classification
verfasst von : Johanna Carvajal-González, AndrésM. Álvarez-Meza, German Castellanos-Domínguez
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
A methodology to classify abandoned objects in video surveillance environments is proposed. Our aim is to determine a set of relevant features that properly describes the main patterns of the objects. Assuming that the abandoned object was previously detected by a visual surveillance framework, a preprocessing stage to segment the region of interest from a given detected object is also presented. Then, some geometric and Hu’s moments features are estimated. Moreover, a relevance analysis is employed to identify which features reveal the major variability of the input space to discriminate among different objects. Attained results over a real-world video surveillance dataset show how our approach is able to select a subset of features for achieving stable classification performance. Our approach seems to be a good alternative to support the development of automated video surveillance systems.