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Proceedings Paper

Target detection and tracking in airborne video imagery using statistical snake and mean shift
Author(s): Shuqun Zhang; Mo Chen
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Paper Abstract

Target detection and tracking in real-time videos are very important and yet difficult for many applications. Numerous detection and tracking techniques have been proposed, typically by imposing some constraints on the motion and image to simplify the problem depending on the application and environment. This paper focuses on target detection and tracking in airborne videos, in which not much simplification can be made. We have recently proposed a combined/switching detection and tracking method which is based on the combination of a spatio-temporal segmentation and statistical snake model. This paper improves the statistical snake model by incorporating both edge and region information and enhancing the snake contour deformation. A more complex motion model is used to improve the accuracy of object detection and size classification. Mean-shift is integrated into the proposed combined method to track small point objects and deal with the problem of object disappearance-reappearance. Testing results using real UAV videos are provided.

Paper Details

Date Published: 16 September 2005
PDF: 8 pages
Proc. SPIE 5909, Applications of Digital Image Processing XXVIII, 590924 (16 September 2005); doi: 10.1117/12.618171
Show Author Affiliations
Shuqun Zhang, CUNY, College of Staten Island (United States)
Mo Chen, State Univ. of New York at Binghamton (United States)


Published in SPIE Proceedings Vol. 5909:
Applications of Digital Image Processing XXVIII
Andrew G. Tescher, Editor(s)

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