8 October 2015 An efficient background modeling approach based on vehicle detection
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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96751A (2015) https://doi.org/10.1117/12.2199349
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
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.
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Jia-yan Wang, Jia-yan Wang, Li-mei Song, Li-mei Song, Jiang-tao Xi, Jiang-tao Xi, Qing-hua Guo, Qing-hua Guo, } "An efficient background modeling approach based on vehicle detection", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96751A (8 October 2015); doi: 10.1117/12.2199349; https://doi.org/10.1117/12.2199349
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