Comparing visual tracker fusion on thermal image sequences

Visual object tracking is a challenging task in computer vision, especially if there are no constraints to the scenario and the objects are arbitrary. The number of tracking algorithms is very large and all have diverse advantages and disadvantages. Normally they show various behaviour and their failures in the tracking process occur at different moments in the sequence. So far, there is no tracker which can solve all scenarios robustly and accurately. One possible approach to this problem is using a whole collection of tracking algorithms and fusing them. There exist various strategies to fuse tracking algorithms. In some of them only the resulting outputs are fused. This means that new algorithms can be integrated with less effort. This fusion can be called "high-level" because the tracking algorithms only interact through the last step in their procedure. Three fusion methods are investigated. They are called Weighted mean fusion, MAD fusion and attraction field fusion. In order to evaluate the three different approaches a collection of thermal image sequences has been investigated. These sequences show maritime scenarios with various objects such as ships and other vessels.