Object Contour Tracking Using Level Sets

High level vision tasks (recognition, understanding, etc.) for video processing require tracking of the complete contour of the objects. In general, objects undergo non-rigid deformations, which limit the applicability of using motion models (e.g. affine, projective) that impose rigidity constraints on the objects. In this paper, we propose a contour tracking algorithm for video captured using mobile cameras of different modalities. The proposed tracking algorithm uses Bayesian inference based on the probability density functions (PDFs) of texture and color features. These feature PDFs are fused in an independent opinion polling strategy, where the contribution of each feature is defined by its discrimination power. We formulate the evolution of the object contour as a variational calculus problem and solve the system using level sets. The associated energy functional combines region-based and boundary-based object segmentation approaches into one framework for object tracking in video, evaluated in the vicinity of the object contour. In this regard, it can be viewed as generalization of formerly proposed methods where the shortcomings of other methods (color, shape, gradient constraints, etc.) are overcome. The robustness of the proposed algorithm is demonstrated on real sequences.