Robust person and object tracking in LWIR and VIS based on a new template matching method

Template matching is one of the oldest techniques in computer vision. It has been applied in a variety of different applications using cross correlation as distance measurement or derivates of it. But so far, the success of object tracking is very limited despite the promising structural similarity search that is done thereby. Based on an analysis of the underlying reasons, a new kind of measurement is proposed therefore to open up far more of the potential the structural search inherently offers. This new measurement does not sum up differences in color space like the cross correlation but outputs the number of matching pixels in percent. As a key feature, local color variations are considered in order to properly handle the different character of homogeneous and highly structured regions and to model the relations between them. Furthermore, relevant differences between templates are expatiated and stressed while irrelevant contributions to the measurement function are widely suppressed in order to avoid unnecessary distortions on the measurement and, therefore, on the search decision. The presented results document the advantages in comparison to the measurements known from the literature. Different objects and persons in LWIR and VIS image sequences are tracked to illustrate the performance and the benefit in a broad field of applications