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.
A preliminary version of this paper appeared in Proceedings of the 2010 Genetic and Evolutionary Computation Conference.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bose, B., Grimson, E.: Improving object classification in far-field video. In: Proceedings of the 2004 IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, pp. 181–188 (2004)
Chen, L., Feris, R., Zhai, Y., Brown, L., Hampapur, A.: An integrated system for moving object classification in surveillance videos. In: Proceedings of the 5th IEEE International Conference on Advanced Video and Signal based Surveillance, Santa Fe, NM, pp. 52–59 (2008)
Cover, T., Hart, P.: Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection, In. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, pp. 886–893 (2005)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2001)
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Paredes, R., Vidal, E.: Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1100–1110 (2006)
Viola, P., Jones, M., Snow, D.: Detecting Pedestrians Using Patterns of Motion and Appearance. International Journal of Computer Vision 63(2), 153–161 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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. https://doi.org/10.1007/978-3-642-24082-9_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-24082-9_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24081-2
Online ISBN: 978-3-642-24082-9
eBook Packages: Computer ScienceComputer Science (R0)