Fast two-cycle curve evolution with narrow perception of background for object tracking and contour refinement

作者：Li, Yaochen; Su, Yuanqi^*; Liu, Yuehu

来源：Signal Processing: Image Communication , 2016, 44: 29-43.

DOI：10.1016/j.image.2016.03.001

• Object tracking
• Contour refinement
• Curve evolution
• Region competition
• Maximum likelihood

The problem of object contour tracking in image sequences remains challenging, especially those with cluttered backgrounds. In this paper, the fast two-cycle level set method with narrow perception of background (FTCNB) is proposed to extract the foreground objects, e.g. vehicles from road image sequences. The curve evolution of the level set method is implemented by computing the signs of region competition terms on two linked lists of contour pixels rather than by solving partial differential equations (PDEs). The curve evolution process mainly consists of two cycles: one cycle for contour pixel evolution and a second cycle for contour pixel smoothness. Based on the curve evolution process, we introduce two tracking stages for the FTCNB method. For coarse tracking stage, the speed function is defined by region competition term combining color and texture features. For contour refinement stage which requires higher tracking accuracy, the likelihood models of the Maximum a posterior (MAP) expressions are incorporated for the speed function. Both the tracking and refinement stages utilize the fast two-cycle curve evolution process with the narrow perception of background regions. With these definitions, we conduct extensive experiments and comparisons for the proposed method. The comparisons with other baseline methods well demonstrate the effectiveness of our work.

• 出版日期2016-5
• 单位西安交通大学