  Contour tracking in adverse environments is a challenging problem due to
cluttered background, illumination variation, occlusion, and noise, among
others. This paper presents a robust contour tracking method by contributing to
some of the key issues involved, including (a) a region functional formulation
and its optimization; (b) design of a robust and effective feature; and (c)
development of an integrated tracking algorithm. First, we formulate a region
functional based on robust Earth Mover's distance (EMD) with kernel density for
distribution modeling, and propose a two-phase method for its optimization. In
the first phase, letting the candidate contour be fixed, we express EMD as the
transportation problem and solve it by the simplex algorithm. Next, using the
theory of shape derivative, we make a perturbation analysis of the contour
around the best solution to the transportation problem. This leads to a partial
differential equation (PDE) that governs the contour evolution. Second, we
design a novel and effective feature for tracking applications. We propose a
dimensionality reduction method by tensor decomposition, achieving a
low-dimensional description of SIFT features called Tensor-SIFT for
characterizing local image region properties. Applicable to both color and
gray-level images, Tensor-SIFT is very distinctive, insensitive to illumination
changes, and noise. Finally, we develop an integrated algorithm that combines
various techniques of the simplex algorithm, narrow-band level set and fast
marching algorithms. Particularly, we introduce an inter-frame initialization
method and a stopping criterion for the termination of PDE iteration.
Experiments in challenging image sequences show that the proposed work has
promising performance.
