Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Multi-dimensional Histogram-Based Image Segmentation
Neural Information Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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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.