Abstract
PTZ (Pan-Tilt-Zoom) cameras are powerful devices in video surveillance applications, because they offer both wide area coverage and highly detailed images in a single device. Tracking with a PTZ camera is a closed loop procedure that involves computer vision algorithms and control strategies, both crucial in developing an effective working system. In this work, we propose a novel experimental framework that allows to evaluate image tracking algorithms in controlled and repeatable scenarios, combining the PTZ camera with a calibrated projector screen on which we can play different tracking situations. We applied such setup to compare two different tracking algorithms, a kernel-based (mean-shift) tracking and a particle filter, opportunely tuned to fit with a PTZ camera. As shown in the experiments, our system allows to finely investigate pros and cons of each algorithm.
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
Agapito, L., Hartley, R., Hayman, E.: Linear self-calibration of a rotating and zooming camera. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 (1999)
Bimbo, A.D., Dini, F., Grifoni, A., Pernici, F.: Uncalibrated framework for on-line camera cooperation to acquire human head imagery in wide areas. In: AVSS 2008: Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp. 252–258. IEEE Computer Society, Washington, DC, USA (2008)
Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal 2(2) (1998)
Cai, Y., de Freitas, N., Little, J.: Robust visual tracking for multiple targets. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 107–118. Springer, Heidelberg (2006)
Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1631–1643 (2005)
Comaniciu, D., Ramesh, V., Meer, P., Member, S., Member, S.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)
Davis, J., Chen, X.: Calibrating pan-tilt cameras in wide-area surveillance networks. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 144–149 (October 2003)
Ellis, A., Shahrokni, A., Ferryman, J.M.: Pets2009 and winter-pets 2009 results: A combined evaluation. In: Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), pp. 1–8 (December 2009)
Greiffenhagen, M., Comaniciu, D., Niemann, H., Ramesh, V.: Design, analysis, and engineering of video monitoring systems: An approach and a case study. PIEEE 89(10), 1498–1517 (2001)
Isard, M., Blake, A.: Condensation: Conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998), doi:10.1023/A:1008078328650
Lieberknecht, S., Benhimane, S., Meier, P., Navab, N.: A dataset and evaluation methodology for template-based tracking algorithms. In: 8th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2009, pp. 145–151 (October 2009)
Ling, H., Wu, Y., Blasch, E., Chen, G., Lang, H., Bai, L.: Evaluation of visual tracking in extremely low frame rate wide area motion imagery. In: 14th Conference on Information Fusion (FUSION, 2011). IEEE, Los Alamitos (2011)
Raimondo, D.M., Gasparella, S., Sturzenegger, D., Lygeros, J., Morari, M.: A tracking algorithm for ptz cameras. In: 2nd IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2010 (September 2010)
Varcheie, P., Bilodeau, G.-A.: People tracking using a network-based ptz camera. Machine Vision and Applications 22, 1–20 (2010), doi:10.1007/s00138-010-0300-1
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), 13:1–13 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salvagnini, P., Cristani, M., Del Bue, A., Murino, V. (2011). An Experimental Framework for Evaluating PTZ Tracking Algorithms. In: Crowley, J.L., Draper, B.A., Thonnat, M. (eds) Computer Vision Systems. ICVS 2011. Lecture Notes in Computer Science, vol 6962. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23968-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-23968-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23967-0
Online ISBN: 978-3-642-23968-7
eBook Packages: Computer ScienceComputer Science (R0)