Evaluation of on-line quality estimators for object tracking
EntityUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
PublisherInstitute of Electrical and Electronics Engineers
10.1109/ICIP.2010.565344917th IEEE International Conference on Image Processing, ICIP 2010. IEEE, 2010. 825-828
ISBN978-1-4244-7993-1 (online); 978-1-4244-7992-4 (print)
Funded byWork 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. Part of the work reported in this paper was done during a research stay of the first author under a research grant (funded by UAM) at Queen Mary University of London (UK).
SubjectsPerformance evaluation without groundtruth; Video surveillance; Visual tracking quality; Telecomunicaciones
NotePersonal 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, "Evaluation of on-line quality estimators for object tracking", in 17th IEEE International Conference on Image Processing, ICIP 2010, p. 825-828
Rights© 2010 IEEE
Failure of tracking algorithms is inevitable in real and on-line tracking systems. The online estimation of the track quality is therefore desirable for detecting tracking failures while the algorithm is operating. In this paper, we propose a taxonomy and present a comparative evaluation of online quality estimators for video object tracking. The measures are compared over a heterogeneous video dataset with standard sequences. Among other results, the experiments show, that the Observation Likelihood (OL) measure is an appropriate quality measure for overall tracking performance evaluation, while the Template Inverse Matching (TIM) measure is appropriate to detect the start and the end instants of tracking failures.
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