Abstract
In this paper we propose implementation of a viable algorithm for real time tracking of objects in a video sequence on a Digital Signal Processor (DSP). Three different tracking algorithms are simulated and on the basis of simulation results, the best algorithm is proposed for hardware implementation. The selected algorithm tracks objects by minimizing the error iteratively. A modification of the selected algorithm is suggested that suits the hardware implementation. The algorithm is tested on different video sequences, both synthetic and real, which demonstrates its performance.
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
Veenman, C., Reinders, M., Backer: Resolving motion correspondence for densely moving points. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 54–72 (2001)
Comaniciu, D., Ramesh, V., Meer: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–575 (2003)
Zhu, S., Yuille: Region competition: unifying snakes, region growing, and bayes/mdl formultiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 884–900 (1996)
Zabih, R., Woodfill, J.: Non parametric local transforms for computing visual correspondence. In: 3rd Europian Conference on Computer Vision, pp. 151–158 (1994)
Haritaoglu, I., Harwood, D., Davis, L.: W4: real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 809–830 (2000)
Owensa, J., Hunterb, A., Fletchera, E.: A fast model-free morphology based object tracking algorithm. In: 13th British Machine Vision Conference, pp. 767–776 (2002)
Shi, J., Tomasi, C.: Good features to track. In: IEEE conference on computer vission and pattern recognition, pp. 593–600 (1994)
Bouguet, J.Y.: (Pyramidal implementation of the lucas kanade feature tracker description of the algorithm)
Birchfield, S.: Derivation of kanade lucas tomasi tracking equation (1997)
Mahlknecht, S., Oberhammer, R., Novak, G.: A real time image recognition system for tiny autonomous mobile robots. Real time systems 29, 247–261 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shah, S.A.A. et al. (2008). Real Time Object Tracking in a Video Sequence Using a Fixed Point DSP. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_87
Download citation
DOI: https://doi.org/10.1007/978-3-540-89646-3_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89645-6
Online ISBN: 978-3-540-89646-3
eBook Packages: Computer ScienceComputer Science (R0)