Target detection and tracking in airborne video imagery using statistical snake and mean shift

Shuqun Zhang; Mo Chen

doi:10.1117/12.618171

16 September 2005 Target detection and tracking in airborne video imagery using statistical snake and mean shift

Shuqun Zhang, Mo Chen

Proceedings Volume 5909, Applications of Digital Image Processing XXVIII; 590924 (2005) https://doi.org/10.1117/12.618171
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

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.

Citation Download Citation

Shuqun Zhang and Mo Chen "Target detection and tracking in airborne video imagery using statistical snake and mean shift", Proc. SPIE 5909, Applications of Digital Image Processing XXVIII, 590924 (16 September 2005); https://doi.org/10.1117/12.618171

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