Artículo de revista
Detection of AIBO and Humanoid Robots using Cascades of Boosted Classifiers
Autor
Arenas, Matías
Ruiz del Solar, Javier
Verschae, Rodrigo
Institución
Resumen
In the present article a framework for the robust detection of mobile
robots using nested cascades of boosted classifiers is proposed. The
boosted classifiers are trained using Adaboost and domain-partitioning
weak hypothesis. The most interesting aspect of this framework is its capability
of building robot detection systems with high accuracy in dynamical
environments (RoboCup scenario), which achieve, at the same time, high
processing and training speed. Using the proposed framework we have built
robust AIBO and humanoid robot detectors, which are analyzed and evaluated
using real-world video sequences.