Genetic algorithms for automatic object movement classification

Authors:
Omid David;Nathan S. Netanyahu;Yoav Rosenberg
Affiliations:
Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel;Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel and Center for Automation Research, University of Maryland, College Park, MD;ProTrack Ltd., Jerusalem, Israel
Venue:
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Year:
2011

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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.