A Robust Moving Objects Detection Based on Improved Gaussian Mixture Model
Xuehua Song, Jingzhu Chen, Chong He, Xiang Sean Zhou · 2010
Gaussian mixture model (GMM) has been widely used for robustly modeling complicated backgrounds. The paper proposes a novel algorithm which can effectively resolve the problems of background disturbance and light changes in allusion to the problem that the background subtraction is sensitive to light changes. The algorithm, by using the idea of construction Histogram for the sample value of each pixel during the course of background initialization and introducing the acceleration factor in the progress of background updating, adopts the Gaussian mixture model to avoid the impact of background disturbance and illumination changes. The algorithm is simulated under the circumstance of background disturbance and illumination changes, the experimental results show that the improved algorithm is more efficient and robust than the traditional methods, and it can achieve background model in the complex environment quickly. The algorithm provides a reliable basis for phase of image tracking and objection categorization.