Share Email Print
cover

Proceedings Paper

An efficient background modeling approach based on vehicle detection
Author(s): Jia-yan Wang; Li-mei Song; Jiang-tao Xi; Qing-hua Guo
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The existing Gaussian Mixture Model(GMM) which is widely used in vehicle detection suffers inefficiency in detecting foreground image during the model phase, because it needs quite a long time to blend the shadows in the background. In order to overcome this problem, an improved method is proposed in this paper. First of all, each frame is divided into several areas(A, B, C and D), Where area A, B, C and D are decided by the frequency and the scale of the vehicle access. For each area, different new learning rate including weight, mean and variance is applied to accelerate the elimination of shadows. At the same time, the measure of adaptive change for Gaussian distribution is taken to decrease the total number of distributions and save memory space effectively. With this method, different threshold value and different number of Gaussian distribution are adopted for different areas. The results show that the speed of learning and the accuracy of the model using our proposed algorithm surpass the traditional GMM. Probably to the 50th frame, interference with the vehicle has been eliminated basically, and the model number only 35% to 43% of the standard, the processing speed for every frame approximately has a 20% increase than the standard. The proposed algorithm has good performance in terms of elimination of shadow and processing speed for vehicle detection, it can promote the development of intelligent transportation, which is very meaningful to the other Background modeling methods.

Paper Details

Date Published: 8 October 2015
PDF: 6 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96751A (8 October 2015); doi: 10.1117/12.2199349
Show Author Affiliations
Jia-yan Wang, Tianjin Polytechnic Univ. (China)
Li-mei Song, Tianjin Polytechnic Univ. (China)
Jiang-tao Xi, Univ. of Wollongong (Australia)
Qing-hua Guo, Tianjin Polytechnic Univ. (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

© SPIE. Terms of Use
Back to Top