DocumentCode
2728052
Title
An improved detection algorithm of moving vehicles based on computer vision
Author
Cao, Jie ; Wang, Wei ; Liang, Yan
Author_Institution
Coll. of Comput. & Commun., Lanzhou Univ. of Technol., Lanzhou, China
Volume
4
fYear
2009
fDate
20-22 Nov. 2009
Firstpage
20
Lastpage
24
Abstract
Moving vehicle detection based on computer vision is an important aspect in the application of Intelligent Transportation Systems (ITS). How to get the accurate parameters of the vehicle in real-time, especially in complicated background situation is very critical. In this paper an improved detection algorithm of moving vehicle is proposed. According to the characteristics of some present detection algorithms, the paper makes improvement to background subtraction and symmetric difference method respectively, and combines both of them. In the method of background difference, the selective statistics background updating algorithm is proposed. Add region-growing segmentation and the morphological filtering methods to the post-processing step. Through simulation experiment with the real traffic video image, the effect is obvious.
Keywords
computer vision; traffic engineering computing; computer vision; intelligent transportation system; morphological filtering; moving vehicles; region-growing segmentation; selective statistics background updating algorithm; traffic video image; vehicle detection algorithm; Application software; Computer vision; Detection algorithms; Filtering; Image segmentation; Intelligent transportation systems; Intelligent vehicles; Statistics; Traffic control; Vehicle detection; background subtraction; computer vision; detection of moving vehicles; selective statistical background updating; symmetrical difference;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-4754-1
Electronic_ISBN
978-1-4244-4738-1
Type
conf
DOI
10.1109/ICICISYS.2009.5357732
Filename
5357732
Link To Document