Detection of AIBO and Humanoid Robots Using Cascades of Boosted Classifiers

Authors:
Matías Arenas;Javier Ruiz-Del-Solar;Rodrigo Verschae
Affiliations:
Department of Electrical Engineering, Universidad de Chile,;Department of Electrical Engineering, Universidad de Chile,;Department of Electrical Engineering, Universidad de Chile,
Venue:
RoboCup 2007: Robot Soccer World Cup XI
Year:
2008

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Abstract

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.