Segmentation-based tracking by support fusion

In this paper we present a novel fusion framework to combine the diverse outputs of arbitrary trackers, which are typically not directly combinable, allowing for significantly increasing the tracking quality. Our main idea is first to transform individual tracking outputs such as motion inliers, bounding boxes, or specific target image features to a shared pixel-based representation and then to run a fusion step on this representation. The fusion process additionally provides a segmentation, which, in turn, further allows for a dynamic weighting of the specific trackers’ contributions. In particular, we demonstrate our fusion concept by combining three diverse heterogeneous tracking approaches that significantly differ in methodology as well as in their reported outputs. In the experiments we show that the proposed fusion strategy can successfully handle highly complex non-rigid object scenarios where the individual trackers and state-of-the-art (non-rigid object and fusion based) trackers fail. We demonstrate high performance on a large number of challenging sequences, where we clearly outperform the individual trackers as well as state-of-the-art tracking approaches.