Proceedings of the AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)

Vehicle Detection in Video Based on the Framework of Kernel Density Estimation

Authors
Dan Yang, Yantao Chen, Richen Liu
Corresponding Author
Dan Yang
Available Online December 2013.
DOI
https://doi.org/10.2991/wiet-13.2013.37How to use a DOI?
Keywords
Background model; Vehicle detection; Kernel density estimation; Intelligent transportation system
Abstract
This paper introduced a method of background subtraction model for detecting vehicles in video, the model was based on the framework of the kernel density estimation. To compute the adjacent frame subtraction on the spatio-temporal by the median filter, when the median value is 0, keep the background pixel value of the point stable, and still use the pixel value of the previous frame of the background to speed up the processing, when the value is not 0, using kernel density estimation to compute the probability, and determine whether update the background pixel value or not. Using histogram to show the statistics of the probability distribution, and find out more accurate threshold of the background adaptively. To solve the problem of deadlock, update the background automatically in a certain period of time. The experiments show that this method can segment the moving vehicles rapidly and accurately from the video.
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Proceedings
AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2013
ISBN
978-90786-77-95-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/wiet-13.2013.37How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Dan Yang
AU  - Yantao Chen
AU  - Richen Liu
PY  - 2013/12
DA  - 2013/12
TI  - Vehicle Detection in Video Based on the Framework of Kernel Density Estimation
BT  - AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/wiet-13.2013.37
DO  - https://doi.org/10.2991/wiet-13.2013.37
ID  - Yang2013/12
ER  -