Fast Two-Cycle level set tracking with narrow perception of background
Yaochen Li, Yuanqi Su, Yuehu Liu · 2015
The problem of tracking foreground objects in a video sequence with moving background remains challenging. In this paper, we propose the Fast Two-Cycle level set method with Narrow band Background (FTCNB) to automatically extract the foreground objects in such video sequences. The level set curve evolution process consists of two successive cycles: one cycle for data dependent term and a second cycle for smoothness regularization. The curve evolution is implemented by computing the signs of region competition terms on two linked lists of contour pixels rather than solving any Partial Differential Equations (PDEs). Maximum A Posterior (MAP) optimization is applied in the FTCNB method for curve refinement with the assistance of optical flows. The comparison with other level set methods demonstrate the tracking accuracy of our method. The tracking speed of the proposed method also outperforms the traditional level set methods.