Motion Target Detection And Tracking Based On Gaussian Background Model

With the development of intelligent video surveillance, many solutions were proposed to detect moving targets from complex scenarios quickly and stably. In view of its advantages on small theory complexity and strong adaptability to different scenarios, background subtraction method became the basic method for motion detection. In the background modeling methods of background subtraction, Gaussian mixtrue model is the classical method in the field for its adaptability, flexibility, efficiency.This paper is based on the background subtraction, systematic introduced the mainstream of moving target detection methods, described single Gaussian model and Gaussian mixture model in background updates mechanism, then done a number of experiments on the important parameters setting of Gaussian mixture model to get experience values in different environments, and accordingly researched a new Gaussian background modeling method based on adaptive update rate. After a lot of comparative tests between the typical algorithm and the new method, it can be demonstrate the new one greatly enhances the speed of modeling. Subsequently, based on the experimental results of the improved algorithm, using the Kalman filtering method to track the moving target, it can be obtain good tracking results.