Chen Zhang, Julian Eggert, Nils Einecke, "Robust Tracking by Means of Template Adaptation with Drift Correction", Proceedings of the 7th International Conference on Computer Vision Systems (ICVS), 2009.

Algorithms for correlation-based visual tracking rely to a great extent on a robust measurement of an object’s location, gained by comparing a template with the visual input. Robustness against object appearance transformations requires template adaptation - a technique that is subject to drift problems due to error integration. Most solutions to this “drift-problem” fall back on a dominant template that remains unmodified, preventing a true adaptation to arbitrary large transformations. In this paper, we present a novel template adaptation approach that instead of recurring to a master template, makes use of object segmentation as a complementary object support to circumvent the drift problem. In addition, we introduce a selective update strategy that prevents erroneous adaptation in case of occlusion or segmentation failure. We show that using our template adaptation approach, we are able to successfully track a target in sequences containing large appearance transformations, where standard template adaptation techniques fail.