Evaluation of on-line quality estimators for object tracking

San Miguel Avedillo, Juan Carlos; Cavallaro, Andrea; Martínez Sánchez, José María

UAM_Biblioteca

Author

San Miguel Avedillo, Juan Carlos

; Cavallaro, Andrea; Martínez Sánchez, José María

Entity

UAM. Departamento de Tecnología Electrónica y de las Comunicaciones

Publisher

Institute of Electrical and Electronics Engineers

Date

2010

Citation

17th IEEE International Conference on Image Processing, ICIP 2010. IEEE, 2010. 825-828

ISSN

1522-4880

ISBN

978-1-4244-7993-1 (online); 978-1-4244-7992-4 (print)

DOI

10.1109/ICIP.2010.5653449

Funded by

Work 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).

Editor's Version

http://dx.doi.org/10.1109/ICIP.2010.5653449

Subjects

Performance evaluation without groundtruth; Video surveillance; Visual tracking quality; Telecomunicaciones

URI

http://hdl.handle.net/10486/667132

Note

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, "Evaluation of on-line quality estimators for object tracking", in 17th IEEE International Conference on Image Processing, ICIP 2010, p. 825-828

Rights

Abstract

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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