An adaptive window object tracking algorithm based on variable resolution

• Autorzy: Li, L.
• Języki publikacji: EN

Abstrakty

EN

This paper presents an adaptive window object tracking method based on variable resolution. It copes with the change in size of the object during visual tracking. On the basis of the visual tracking algorithm, based on maximum posterior probability, we analyze the posterior probability index on the inside and outside panes of the object window, then build a mathematical model for adjusting object size with an adaptive window. Since the resolution changes according to the size of the object, this thesis uses a statistical sampling method of the feature by variable resolution. The resolution of the statistical feature is correspondingly changed in object tracking with an adaptive window. The resolution of a larger object is decreased, which realizes an object tracking method with adaptive window based on variable resolution.

Słowa kluczowe

EN

visual tracking; adaptive window; variable resolution; maximum posterior probability

• Czasopismo: Opto-Electronics Review
• Rocznik: 2011
• Tom: Vol. 19, No. 2
• Strony: 219-224
• Opis fizyczny: Bibliogr. 8 poz., il., wykr.

Twórcy

autor

Li, L.

• College of Computer Science, Shaanxi Normal University, Xi'an, 710062 China,

Bibliografia

• [1] M. Yang, Z. Fan, J. Fan, and Y. Wu: Tracking nonstationary visual appearances by data-driven adaptation. IEEE T. Image Process. 18, 1633-1644, 2009.
• [2] D.Y. Chen: Implementation of maneuver target prediction in image tracking system. J. Appl. Optics 28, 33-37, 2007.
• [3] D. Comaniciu, V. Ramesh, and P. Meer: Kernel-based object tracking. IEEE T. Pattern Anal. 25, 564-577, 2003.
• [4] D. Comaniciu: An algorithm for data-driven bandwidth selection. IEEE T. Pattern Anal. 25, 281-288, 2003.
• [5] N.S. Peng, J. Yang, and J.X. Chen: Kernel-bandwidth adaptation for tracking object changing in size. Int. Conf. Image Anal. Recogn. 2, 581-588, 2004.
• [6] R.T. Collins: Mean-shift blob tracking through scale space. Proc. CVPR IEEE 2, II-234-40, 2003.
• [7] D. Comaniciu, V. Ramesh, and P. Meer: The variable bandwidth mean shift and data-driven scale selection. IEEE I. Conf. Comp. Vis. 1, 438-445, 2001.
• [8] Z. Feng, N. Lu, and L. Li: Research on image matching similarity criterion based on maximum posterior probability. Acta Automatica Sinica 33, 1-8, 2007.