Standalone evaluation of deterministic video tracking
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics EngineersDate
2012Citation
10.1109/ICIP.2012.6467119
19th IEEE International Conference on Image Processing, ICIP 2012. IEEE, 2012. 1353-1356
ISSN
1522-4880ISBN
978-1-4673-2532-5 (online); 978-1-4673-2534-9 (print)DOI
10.1109/ICIP.2012.6467119Funded by
Work partially supported by the Spanish Government (TEC2011-25995 EventVideo), by the Consejería de Educación of the Comunidad de Madrid, and by The European Social Fund.Editor's Version
http://dx.doi.org/10.1109/ICIP.2012.6467119Subjects
Performance evaluation without ground-truth; Uncertainty estimation; Visual tracking; Informática; TelecomunicacionesNote
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. SanMiguel, A. Cavallaro, and J. M. Martínez, "Standalone evaluation of deterministic video tracking", in 19th IEEE International Conference on Image Processing (ICIP), 2012. Orlando, pp. 1353 - 1356Rights
© 2012 IEEEAbstract
We present an approach for performance evaluation of deterministic video trackers without ground-truth data. The proposed approach detects if a tracker is correctly operating over time using two main steps. First, it transforms the output of the localization step into a distribution of the target state, which emulates a multi-hypothesis tracker. Then, the uncertainty of such distribution is estimated to determine the time instants when the tracker is stable. A time-reversed analysis is used to identify tracker recovery after unsuccessful operation. The proposed approach is demonstrated on the well-known MeanShift tracker. The results over a heterogeneous dataset show that the proposed approach outperforms the related state-of-the-art methods in presence of tracking challenges such as occlusions, illumination and scale changes, and clutter.
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Google Scholar:San Miguel Avedillo, Juan Carlos
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Cavallaro, Andrea
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Martínez Sánchez, José María
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