Moving Objects Detection Method Based on a Fast Convergence Gaussian Mixture Model
Background Modeling is the normal method on the moving objects detection, it plays a key role in the moving objects detection and tracking, the Gaussianmixture model is one of the most successful methods on the detection. But it converges slowly in the complex scene. This paper proposes a new method namedadditive increase and the additive decreaseto adjust the weight of the matched distributions and the unmatched distributions respectively. The method can speed up the mixture model convergence process. In order to reduce the noise, noise restraint base on the adjacent regionapproach is using to increase the probability of classifying each pixel correctly during the moving objects detection.
Gaussian mixture model Fast convergence additive increase additive decrease object detection noise restraint
Jin Wang Lanfang Dong
School of Computer Science and Technology University of Science and Technology of China Hefei, China
国际会议
上海
英文
269-273
2011-03-11（万方平台首次上网日期，不代表论文的发表时间）