Robot detection with a cascade of boosted classifiers based on haar-like features

Authors:
F. Serhan Daniş;Tekin Meriçli;Ç etin Meriçli;H. Levent Akin
Affiliations:
Boğaziçi University, Department of Computer Engineering, Bebek, Istanbul, Turkey;Boğaziçi University, Department of Computer Engineering, Bebek, Istanbul, Turkey;Boğaziçi University, Department of Computer Engineering, Bebek, Istanbul, Turkey;Boğaziçi University, Department of Computer Engineering, Bebek, Istanbul, Turkey
Venue:
RoboCup 2010
Year:
2011

Citing 3
Cited 1

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Abstract

Accurate world modeling is important for efficient multi-robot planning in robot soccer. Visual detection of the robots on the field in addition to all other objects of interest is crucial to achieve this goal. The problem of robot detection gets even harder when robots with only on board sensing capabilities, limited field of view, and restricted processing power are used. This work extends the real-time object detection framework proposed by Viola and Jones, and utilizes the unique chest and head patterns of Nao humanoid robots to detect them in the image. Experiments demonstrate rapid detection with an acceptably low false positive rate, which makes the method applicable for real-time use.