Abstract
Many trackers have been proposed for tracking objects individually in previous research. However, it is still difficult to trust any single tracker over a variety of circumstances. Therefore, it is important to estimate how well each tracker performs and fusion the tracking results. In this paper, we propose a symbiotic black-box tracker (SBB) that learns only from the output of individual trackers, which run in parallel, without any detailed information about these trackers and selects the best one to generate the tracking result. All trackers are considered as black-boxes and SBB learns the best combination scheme for all existing tracking results. SBB estimates confidence scores of these trackers. The confidence score is estimated based on the tracking performance of each tracker and the consistency performance among different trackers. SBB is employed to select the best tracker with the maximum confidence score. Experiments and comparisons conducted on the “Caremedia” dataset and the “Caviar” dataset demonstrate the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Journal of Computing Surveys 38(1) (2006)
Reid, D.: An algorithm for tracking multiple targets. IEEE Trans. on Automatic Control, IEEE society 24(6), 843–854 (1979)
Avidan, S.: Ensemble Tracking. IEEE Transaction of Pattern Analysis and Machine Intelligence 29(2), 261–271 (2007)
Rasmussen, C., Hager, G.: Joint probabilistic techniques for tracking objects using multiple visual cues. In: Porc. Intelligent Robots and Systems, vol. 1, pp. 191–196 (1998)
Badrinarayanan, V., Perez, P., Clerc, F.: Probabilistic color and adaptive multi-feature tracking with dynamically switched priority between cues. In: Proc. International Conference on Computer Vision (2007)
Stenger, B., Woodley, T., Cipolla, R.: Learning to track with multiple observers. In: Proc. Computer Vision and Pattern Recognition, pp. 2647–2654 (2009)
Zhong, B., Yao, H., Chen, S., Ji, R., Yuan, X., Liu, S., Gao, W.: Visual tracking via weakly supervised learning from multiple imperfect oracles. In: Proc. Computer Vision and Pattern Recognition, pp. 1323–1330 (2010)
Du, W., Piater, J.H.: A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 225–238. Springer, Heidelberg (2008)
Grabner, H., Bischof, H.: On-line boosting and vision. In: Proc. Computer Vision and Pattern Recognition, vol. (1), pp. 260–267 (2006)
Babenko, B., Yang, M., Belongie, S.: Visual Tracking with Online Multiple Instance Learning. In: Proc. Computer Vision and Pattern Recognition, pp. 983–990 (2009)
Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-Tracking Using Semi-Supervised Support Vector Machines. In: Proc. International Conference on Computer Vision, pp. 1–8 (2007)
Stalder, S., Grabner, H., Gool, L.: Beyond Semi-Supervised Tracking:Tracking Should Be as Simple as Detection, but not Simpler than Recognition. In: Proc. International Conference on Computer Vision 2009 Workshop on On-line Learning for Computer Vision (2009)
Perez, P., Vermaak, J., Blake, A.: Data fusion for visual tracking withparticles. Proceedings of the IEEE 92(3), 495–513 (2004)
Moreno-Noguer, F., Sanfeliu, A., Samaras, D.: Dependent multiple cue integration for robust tracking. IEEE Trans. Pattern Analysis and Machine Intelligence. 30(1), 670–685 (2008)
Leichter, I., Lindenbaum, M., Rivlin, E.: A generalized framework for combining visual trackers - the black boxes approach. International Journal of Computer Vision 67(2), 91–110 (2006)
Beugnon, G., Singh, T., Llinnas, J., Saha, R.K.: Adaptive Tracking Fusion in a Multisensor Environment. In: Proc. International Conference on Information Fusion, vol. (1), pp. 24–31 (2000)
Chee, C.Y., Mori, S., Barker, W.H., Chang, K.C.: Architectures and Algorithms for Track Association and Fusion. IEEE Aerospace and Electronic Systems Magazine 15(1), 5–13 (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proc. Computer Vision and Pattern Recognition (2000)
Nummiaroa, K., Meierb, E., Gool, L.: An adaptive color-based particle filter. Image and Vision Computing 21(1), 99–110 (2003)
Stevens, S., Chen, D., Wactlar, H., Hauptmann, A., Christel, M., Bharucha, A.: Automatic Collection, Analysis, Access and Archiving of Psycho/Social Behavior by Individuals and Groups. In: Proc. of the 3rd ACM Workshop on Continuous Archival and Retrival of Personal Experences (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, L., Gao, Y., Hauptmann, A., Ji, R., Ding, G., Super, B. (2012). Symbiotic Black-Box Tracker. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-27355-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27354-4
Online ISBN: 978-3-642-27355-1
eBook Packages: Computer ScienceComputer Science (R0)