Abstract
Object tracking is fundamental to automated video surveillance, activity analysis and event recognition. Only a small percentage of the system resources can be allocated for tracking in real time applications, the rest being required for high-level tasks such as recognition, trajectory interpretation, and reasoning. There is a desperate need to carefully optimize the tracking algorithm to keep the computational complexity of a tracker as low as possible yet maintaining its robustness and accuracy. This paper proposes a novel algorithm which attempts to attain a light weight tracking system by reducing undesirable and redundant computations. The frames of the video are passed through a preprocessing stage which transmits only motion detected blocks to the tracking algorithm. Further frames containing little motion in the search area of the target object are detected in preprocessing stage itself and are blocked from further processing. Our experimental results demonstrate that the throughput of the new proposed tracking system is exceptionally higher than the traditional one.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hu, W., Wang, L.T., Maybank, S.: A Survey on visual surveillance on object motion and behaviours. IEEE Transactions on systems, man and cybernatics 34(3), 334–352 (2004)
Greiffenhagen, M., Commaniciu, D., Niemann, H., Ramesh, V.: Design, Analysis and Engineering of Video Monitoring Systems: An approach and a case study. Proc. IEEE, 1498–1517 (2001)
Collins, R., Lipton, A., Fujiyoshi, H., Kanade, T.: Algorithms for Cooperative Multisensor Surveillance. Proc. IEEE 89(10), 1456–1477 (2001)
Ferrari, V., Tuytelaars, T., Gool, L.V.: Real-Time Affine Regiion Tracking and Coplanar Grouping. In: Proc. IEEE Conference Computer Vision and Pattern Recognition, vol. 2, pp. 226–233 (2001)
Bue, A.D., Commaniciu, D., Ramesh, V., Regazzoni, C.: Smart Cameras with Real-Time Video Object Generation. In: Proc. of International Conference on Image Processing, vol. 3, pp. 429–432 (2002)
Yilmaz, A., Javed, O., Shah, M.: Object tracking- a survey. ACM Computer Surveys 38(4), 1–45 (2006)
Commaniciu, D., Ramesh, V., Meer, P.: Kernel Based Object Tracking. IEEE Trans. on Pattern Anal. and Machine Intell. 22, 781–796 (2000)
Han, S.A., Hua, W., Gong, Y.: A Detection Based multiple Object Tracking Method. In: Proc. IEEE International Conference on Image Processing (2004)
McKenna, S.J., Jabri, S., Duric, Z., Wechsler, H.: Tracking Interacting people. In: Proc. of the International Conf. on Automatic Face and Gesture Recognition, Grenoble, France, March 28-30, pp. 348–353 (2000)
Dick, A.R., Brooks, M.J.: Issues in automated visual surveillance. In: DICTA, Sydney, NSW, Australia, pp. 195–204 (2003)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. IEEE Conf. Computer Vision and Pattern Recog., vol. 2, pp. 246–252 (1999)
Kim, K., Harwood, D., Davis, L.S.: Background updating for visual surveillance. In: First Int. Symosium on Visual Computing, ISVC, Lake Tahoe, Nevada, pp. 337–346 (2005)
Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving Target classification and tracking from a real-time video. In: Proc. IEEE Workshop Applications of Computer Vision, pp. 8–14 (1998)
Cutler, R., Davis, L.S.: Robust real-time periodic motion detection, analysis and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 781–796 (2000)
Tao, H., Sawhney, H.S., Kumar, R.: Object Tracking with Bayesian Estimation of Dynamic Layer Representations. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 75–89 (2002)
Black, J., Ellis, T., Rosin, P.: A novel method for video tracking performance evaluation. In: Joint IEEE Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp. 125–132 (2003)
Moeslund, T., Franum, E.: A survey of computer vision-based human motion capture. Comput. Vision Image Understand 81(3), 231–268 (2001)
Mittal, A., Davis, L.: M2 tracker: A multiview approach to segmenting and tracking people in a cluttered scene. Int. J. Comput. Vision 51(3), 189–203 (2003)
Maddalena, L., Petrosino, A.: Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. IEEE Transactions on Image Processing 17(7), 1168–1177 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Datla, S., Agarwal, A., Niyogi, R. (2010). A Novel Algorithm for Achieving a Light-Weight Tracking System. In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14834-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-14834-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14833-0
Online ISBN: 978-3-642-14834-7
eBook Packages: Computer ScienceComputer Science (R0)