Contour extraction and tracking in video using a joint similarity measure

Abstract

In this paper, an active contour model based on the gradient vector flow of the gray-level and the motion similarity measure (MGVF-Snake) is introduced after analyzing the performance of the traditional Snake and Snake based on gradient vector flow (GVF-Snake), the algorithm is proposed to extract and track the Video Object (VO) automatically. The MGVF-Snake overcomes the shortcoming of the GVF-Snake that could not fine the VO contour precisely in the complex background. In allusion to the problem that the traditional GVF-Snake easily makes mistake when tracing the VO moving rapidly, the scheme takes advantage of the redundancy and makes the tracking more accurately and rapidly by adjusting the previous VO contour with the motion vector as the current initial contour. The algorithm is validated with the video sequences, the results of the experimentation show that it not only extract VO contour automatically, but also track accurately.