Abstract
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.
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 language | English |
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Title of host publication | WACV 2016: IEEE Winter Conference on Applications of Computer Vision |
Publisher | IEEE Computer Society Press |
DOIs | |
Publication status | Published - Mar 2016 |
Event | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) - Lake Placid, NY, USA, Lake Placid, NY, United States Duration: 7 Mar 2016 → 10 Mar 2016 |
Conference
Conference | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) |
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Country/Territory | United States |
City | Lake Placid, NY |
Period | 7/03/16 → 10/03/16 |