Genetic Algorithms for Automatic Object Movement Classification

  • Omid David
  • Nathan S. Netanyahu
  • Yoav Rosenberg
Conference paper

DOI: 10.1007/978-3-642-24082-9_32

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6935)
Cite this paper as:
David O., Netanyahu N.S., Rosenberg Y. (2011) Genetic Algorithms for Automatic Object Movement Classification. In: Lee G., Howard D., Ślęzak D. (eds) Convergence and Hybrid Information Technology. ICHIT 2011. Lecture Notes in Computer Science, vol 6935. Springer, Berlin, Heidelberg

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.

Keywords

Genetic algorithms Parameter tuning Computer vision Automatic object recognition Movement classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Omid David
    • 1
  • Nathan S. Netanyahu
    • 1
    • 2
  • Yoav Rosenberg
    • 3
  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat-GanIsrael
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA
  3. 3.ProTrack Ltd.JerusalemIsrael

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