Learning Motion Detectors by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DBLP:conf/ausai/PintoS09,

• author = "Brian Pinto and Andy Song",
• title = "Learning Motion Detectors by Genetic Programming",
• booktitle = "Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09)",
• year = "2009",
• editor = "Ann E. Nicholson and Xiaodong Li",
• volume = "5866",
• series = "Lecture Notes in Computer Science",
• pages = "160--169",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• address = "Melbourne, Australia",
• month = dec # " 1-4",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-10438-1",
• DOI = "doi:10.1007/978-3-642-10439-8_17",
• 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