DocumentCode :
2917090
Title :
Fast video analysis by genetic programming
Author :
Song, Andy
Author_Institution :
Sch. of Comput. Sci. & Inf. Technol., RMIT Univ., Melbourne, VIC
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
3237
Lastpage :
3243
Abstract :
Genetic programming has been applied to various types of vision tasks. This paper extends the use of this powerful problem solving method to a more complex but more common domain, video analysis. We present the methodology as well as the experiments on two video analysis tasks: segmenting texture regions and detecting moving objects. The advantages of GP in this domain can be shown by this study. Firstly GP methods are less dependent on knowledge from domain experts. One methodology is suitable for both tasks. Secondly GP can generate fast video frame analyzers which are highly desirable or even critical in real time vision applications.
Keywords :
genetic algorithms; image segmentation; image texture; object detection; video signal processing; fast video analysis; genetic programming; moving object detection; texture region segmentation; vision tasks; Data mining; Delay; Face detection; Genetic programming; Information analysis; Mobile handsets; Motion detection; Object detection; Problem-solving; Streaming media;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4631236
Filename :
4631236
Link To Document :
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