Robust Tracking by Means of Template Adaptation with Drift Correction

Abstract

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.