Genetic algorithms for automatic classification of moving objects

  • Authors:
  • Omid David-Tabibi;Nathan S. Netanyahu;Yoav Rosenberg;Moshe Shimoni

  • Affiliations:
  • Bar-Ilan University, Ramat-Gan, Israel;Bar-Ilan University, Ramat-Gan, Israel;ProTrack Ltd., Jerusalem, Israel;ProTrack Ltd., Jerusalem, Israel

  • Venue:
  • Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
  • Year:
  • 2010

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Abstract

This paper presents an integrated approach, combining a state-of-the-art commercial object detection system and genetic algorithms (GA)-based learning for automatic object classification. Specifically, the approach is based on applying weighted nearest neighbor classification to feature vectors extracted from the detected objects, where the weights are evolved due to GA-based learning. Our results demonstrate that this GA-based approach is considerably superior to other standard classification methods.