Adaptive online performance evaluation of video trackers
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics EngineersDate
2012-05Citation
10.1109/TIP.2011.2182520
IEEE Transactions on Image Processing 21.5 (2012): 2812 - 2823
ISSN
1057-7149 (print); 1941-0042 (online)DOI
10.1109/TIP.2011.2182520Funded by
This work was partially supported by the Spanish Government (TEC2007- 65400 SemanticVideo), Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, Consejería de Educación of the Comunidad de Madrid and European Social Fund.Editor's Version
http://dx.doi.org/10.1109/TIP.2011.2182520Subjects
Failure detection; Particle filter; Time reversibility; Track quality; Tracking uncertainty; Video 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. Caballaro, and J. M. Martínez, "Adaptive Online Performance Evaluation of Video Trackers", IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2812 - 2823. May 2012Rights
© 2012 IEEEAbstract
We propose an adaptive framework to estimate the quality of video tracking algorithms without ground-truth data. The framework is divided into two main stages, namely, the estimation of the tracker condition to identify temporal segments during which a target is lost and the measurement of the quality of the estimated track when the tracker is successful. A key novelty of the proposed framework is the capability of evaluating video trackers with multiple failures and recoveries over long sequences. Successful tracking is identified by analyzing the uncertainty of the tracker, whereas track recovery from errors is determined based on the time-reversibility constraint. The proposed approach is demonstrated on a particle filter tracker over a heterogeneous data set. Experimental results show the effectiveness and robustness of the proposed framework that improves state-of-the-art approaches in the presence of tracking challenges such as occlusions, illumination changes, and clutter and on sequences containing multiple tracking errors and recoveries.
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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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