Towards Automated Learning of Object Detectors
Eberhard-Karls-Universitat Tubingen, Wilhelm-Schickard-Institut fur Informatik, Abt. Rechnerarchitektur, Sand 1, 72076 Tubingen, Germany
Applications of Evolutionary Computation, Lecture Notes in Computer Science, 2010, Volume 6024/2010, 231-240
@article{ebner2010towards,
title={Towards automated learning of object detectors},
author={Ebner, M.},
journal={Applications of Evolutionary Computation},
pages={231–240},
year={2010},
publisher={Springer}
}
Recognizing arbitrary objects in images or video sequences is a difficult task for a computer vision system. We work towards automated learning of object detectors from video sequences (without user interaction). Our system uses object motion as an important cue to detect independently moving objects in the input sequence. The largest object is always taken as the teaching input, i.e. the object to be extracted. We use Cartesian Genetic Programming to evolve image processing routines which deliver the maximum output at the same position where the detected object is located. The graphics processor (GPU) is used to speed up the image processing. Our system is a step towards automated learning of object detectors.
January 31, 2011 by hgpu
