中文摘要：

为了解决当前目标跟踪中目标轮廓提取不精确的问题，在对传统GVF（gradientvectorflow）snake活动轮廓模型改进的基础上，提出一种基于变化检测和改进的GVFsnake活动轮廓模型的视频目标轮廓提取算法。首先，通过基于t显著性检验的变化检测方法消除背景边界的影响，并获取初始运动变化区域的临界四边形作为GVFsnake的初始轮廓。然后，对初始轮廓应用改进的GVFsnake模型以获得精确的轮廓边界。改进模型采用4方向各项异性扩散，并采用下降速度较快的保真项系数以增强GVFsnake进入凹陷的能力，且保持对弱边界的收敛。本文方法克服了手动绘制初始轮廓的缺点，对传统GVFsnake方法进行了改进，且空间准确度（SA）有很大提高。实验表明，本文方法成功分割出目标凹陷部分并对弱边界有较好的收敛效果，提高了轮廓提取的精确度。

英文摘要：

To deal with the problem of inaccuracy in current object contour extraction algorithms, a new approach based on improved gradient vector flow （GVF） snake and t-distribution significance test based change detection is proposed. It firstly eliminates most background boundaries through t-distribution significance test based change detection,an enclosed rectangle is obtained,and the rectangle is set to be the original boundary of the GVF snake model, which is obtained by calculating four critical points of the change detection mask. Then the improved GVF snake model is applied to the rough boundary to get the precise boundary. The improved GVF snake adopts anisotropic diffusion and a four-direction edge operator to solve the blurry boundary and edge shifting caused by the traditional GVF snake, and makes use of a new coeffident of fidelity term which has a faster descent speed to strengthen snake to segment the concave part. The proposed algorithm overcomes the disadvantages of drawing the initial contour manu ally. Furthermore, the GVF snake method in the spatial domain has been improved with higher accuracy. Experimental results indicate that the new proposed method gains accurate segmentation results in both the concave region and the weak edge part. Experimental results with several test video sequences indicate the validity and extraction accuracy.