| Abstract | dc.description.abstract | The visual detection of robots is a difficult but relevant problem in several robotic applications. In the
present article, a framework for the robust and fast visual detection of legged-robots is proposed. This
framework uses cascades of nested classifiers, the Adaboost boosting algorithm, and domainpartitioning
based weak classifiers. Using the proposed framework, frontal, profile and back detectors
for AIBO robots (model ERS7), as well as detectors for humanoid robots, are built. The detection rate
of the obtained systems is quite high: 90% with an average of 0.1 false positives per image, when the
final detections are filtered out using context information (horizon line). In addition, a robot referee
that uses these detectors to track players during a soccer game is described. Experiment results showed
that the referee achieves very high robot detection rates (98.7% DR with ~1 false detection every 16
images), and fast processing speed. | es_CL |
