Abstract
In order to tracking moving objects of aerial images, the frames and the scene is kept space consistency through image registration at the background. Due to high image resolution and large geographic deformation between different frames of aerial video, complicating the image registration. An piecewise planar region matching based image registration is introduced that can subdivide large frame into planar region, Image subdivision reduces the geographic distortions between aerial video, as it is usually the case of high-resolution aerial images. Then we can use select the most “useful” matching points that best satisfy the affine invariant space constraints are used to estimate the transformation model and register the images in a piecewise manner. Experiment result illustrate that the proposed method can register the high-resolution images and track the moving object in an aerial video.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)
Ali, S., Reilly, V., Shah, M.: Sneddon: motion and appearance contexts for tracking and reacquiring targets in aerial videos. In: IEEE CVPR, pp. 1–6 (2007)
Parag, T., Elgammal, A., Mittal, A.: A framework for feature selection for background subtraction. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1916–1923 (2006)
Shum, H.-Y., Szeliski, R.: Construction of panoramic image mosaics with global and local alignment. Int. J. Comput. Vis. 36(2), 101–130 (2000)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 15(6), 415–434 (1997)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 506–513 (2004)
Krish, K., Heinrich, S., Snyder, W.E.: Global registration of overlapping images using accumulative image features. Pattern Recogn. Lett. 31(2), 112–118 (2010)
Rothwell, C.A., Zisserman, A.: Using projective invariants for constant time library indexing in model based vision. In: BMVC 1991 (1991)
Bay, H., Tuytelaars, T., Van Gool, L: SURF: speeded up robust features. In: Proceedings of the Ninth European Conference on Computer Vision, May 2008
Mundy, J.L., Heller, A.: Geometric Invariance in Computer Vision. MIT Press, Cambridge (1992)
Kahl, F., Heyden, A.: Using conic correspondences in two images to estimate the epipolar geometry. In: Proceedings of the International Conference on Computer Vision (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yi, M., Sui, Lc. (2017). Piecewise Planar Region Matching for High-Resolution Aerial Video Tracking. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-48499-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48498-3
Online ISBN: 978-3-319-48499-0
eBook Packages: EngineeringEngineering (R0)