Robust visual tracking using template anchors

Luka Čehovin, Ales Leonardis, Matej Kristan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)
277 Downloads (Pure)


Deformable part models exhibit excellent performance in tracking non-rigidly deforming targets, but are usually outperformed by holistic models when the
target does not deform or in the presence of uncertain visual data. The reason is that part-based models require estimation of a larger number of parameters compared to holistic models and since the updating process is self-supervised, the errors in parameter estimation are amplified with time, leading to a faster accuracy reduction than in holistic models. On the other hand, the robustness of part-based trackers is generally greater than in holistic trackers. We address the
problem of self-supervised estimation of a large number of parameters by introducing controlled graduation in estimation of the free parameters. We propose decomposing the visual model into several sub-models, each describing the target at a different level of detail. The sub-models interact during target localization and, depending on the visual uncertainty, serve for cross-sub-model supervised updating. A new tracker is proposed based on this model which exhibits the qualities of part-based as well as holistic models. The tracker is tested on the highly-challenging VOT2013 and VOT2014 benchmarks, outperforming the state-of-the-art.
Original languageEnglish
Title of host publicationWACV 2016: IEEE Winter Conference on Applications of Computer Vision
PublisherIEEE Computer Society Press
Publication statusPublished - Mar 2016
Event2016 IEEE Winter Conference on Applications of Computer Vision (WACV) - Lake Placid, NY, USA, Lake Placid, NY, United States
Duration: 7 Mar 201610 Mar 2016


Conference2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
Country/TerritoryUnited States
CityLake Placid, NY


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