Moving object detection and tracking based on geodesic active contour model
采用测地线活动轮廓模型检测与跟踪运动目标

The geometric-active contour model based on the level set can better handle the variations of the curve topology. In order to track a rigid or non-rigid moving object and extract its contour information, we propose a combination method of the improved geodesic active contour (GAC) model and Kalman filter. In this method, the moving regions of the object are determined by using Gaussian mixture model and the background difference method; the GAC model with a distance regularization term is used to perform the curve evolution in the moving region, making the evolving curve approaching to the true contours of the object. The tracking of the moving object is realized by using Kalman filter to predict the object position of the next frame. Experimental results show that the proposed method is applicable to both rigid and non-rigid objects, achieving good detection and tracking effect even in the case of partial occlusion.