Efficient Rotation Invariant Object Detection using Boosted Random Ferns.

Efficient Rotation Invariant Object Detection using Boosted Random Ferns. M. Villamizar, F. Moreno-Noguer, J. Andrade-Cetto, A. Sanfeliu. Conference in Computer Vision and Pattern Recognition (CVPR), 2010. [Results on the IRI Freestyle Motocross Dataset (Test 2)] Description: The method makes use of a simple two-step approach with an estimation stage and a classification stage. The estimator yields an initial set of potential object poses that are then validated by the classifier. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The method is based on a boosted combination of Random Ferns over local histograms of oriented gradients -HOGs-. Green rectangles indicate correct detections -true positives-, whereas red rectangles are false positives. Blue rectangles correspond to the ground truth. Links: http://www.iri.upc.edu/people/mvillami/cvpr10.html Contact: Michael Villamizar mvillami-at-iri.upc.edu Institut de Robòtica i Informática Industrial CSIC-UPC Barcelona - Spain

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