Learning Motion Detectors by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{DBLP:conf/ausai/PintoS09,
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author = "Brian Pinto and Andy Song",
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title = "Learning Motion Detectors by Genetic Programming",
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booktitle = "Proceedings of the 22nd Australasian Joint Conference
on Artificial Intelligence (AI'09)",
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year = "2009",
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editor = "Ann E. Nicholson and Xiaodong Li",
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volume = "5866",
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series = "Lecture Notes in Computer Science",
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pages = "160--169",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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address = "Melbourne, Australia",
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month = dec # " 1-4",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-10438-1",
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DOI = "doi:10.1007/978-3-642-10439-8_17",
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abstract = "Motion detection in videos is a challenging problem
that is essential in video surveillance, traffic
monitoring and robot vision systems. In this paper, we
present a learning method based on Genetic
Programming(GP) to evolve motion detection programs.
This method eliminates the need for pre-processing of
input data and minimises the need for human expertise,
which are usually critical in traditional approaches.
The applicability of the GP-based method is
demonstrated on different scenarios from real world
environments. The evolved programs can not only locate
moving objects but are also able to differentiate
between interesting and uninteresting motion.
Furthermore, it is able to handle variations like
moving camera platforms, lighting condition changes,
and cross-domain applications.",
- }
Genetic Programming entries for
Brian Pinto
Andy Song
Citations