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Track-based self-supervised classification of dynamic obstacles
<span title="2010-06-05">2010</span>
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This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that relies on the stability of a subset of features given by a sensor to perform an initial robust classification based on unsupervised techniques. The obtained results are used as labels to train a set of supervised classifiers. The outcomes obtained with the second sensor can be used for higher level tasks such as segmentation or
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... to refine the within-clusters discrimination. The proposed architecture is evaluated for a particular realization based on range and visual information which produces track-based labeling that is then employed to train supervised modules that perform instantaneous classification. Experiments show that the system is able to achieve 95% classification accuracy and to maintain the performance through on-line retraining when working conditions change.
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