ICRA'09 Paper Abstract


Paper FrB2.2

Villamizar Vergel, Michael (CSIC-UPC), Scandaliaris, Jorge (CSIC-UPC), Sanfeliu, Alberto (Universitat Politècnica de Cataluyna), Andrade-Cetto, Juan (CSIC-UPC)

Combining Color-Based Invariant Gradient Detector with HOG Descriptors for Robust Image Detection in Scenes under Cast Shadows

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation - III" (FrB2), Friday, May 15, 2009, 10:50−11:10, Room: ICR

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Computer Vision for Robotics and Automation, Visual Tracking


In this work we present a robust detection method in outdoor scenes under cast shadows using color based invariant gradients in combination with HOG local features. The method achieves good detection rates in urban scene classification and person detection outperforming traditional methods based on intensity gradient detectors which are sensible to illumination variations but not to cast shadows. The method uses color based invariant gradients that emphasize material changes and extract relevant and invariant features for detection while neglecting shadow contours. This method allows to train and detect objects and scenes independently of scene illumination, cast and self shadows. Moreover, it allows to do training in one shot, that is, when the robot visits the scene for the first time.



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