Learning distance metric for object contour tracking

Contour tracking can be implemented by measuring the probability distributions (e.g. intensity, color and texture) of both interior and exterior regions of an object contour. Choosing a suitable distance metric for measuring the (dis)similarity between two distributions significantly influences the tracking performance. Most existing contour tracking methods, however, utilize a predefined metric which may not be appropriate for measuring the distributions. This paper presents a novel variational level set framework for contour tracking. The image energy functional is modeled by the distance between the foreground distribution and the given template, divided by the distance between the background distribution and the template. The form of the distance between two distributions is represented by the quadratic distance (Rubner et al. in Int J Comput Vis 40(2):99–121, 2000). To obtain the more robust tracking results, a distance metric learning algorithm is employed to achieve the similarity matrix for the quadratic distance. In addition, a distance between the evolving contour and the zero level set of the reference shape function is adopted as the shape prior to constrain the contour evolution process. Experiments on several video sequences prove the effectiveness and robustness of our method.