Rotation-Invariant Fast Feature Based Image Registration for Motion Compensation in Aerial Image Sequences

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 14)

Abstract

Motion compensation can be used as a preprocessing step in the application of object tracking in aerial image sequences from Unmanned Air Vehicle to cancel the effect of camera motion. In this paper, we demonstrate Aerial Image Registration that gives high degree of accuracy for motion compensation. Rotation Invariant Fast Features that use approximate radial gradient transform are used to reduce the computation time of feature extraction considerably. These descriptors well define the aerial image features taken from platforms like UAV that are prone to high degree of rotation due to sudden maneuver, scaling, illumination change and noise. Another contribution of the paper is in the formulation of new framework for set based registration of aerial images. Results using the group scheme outperform the usual pair wise registration and demonstrate real-time performance.

Keywords

Motion compensation Image registration Rotation invariant fast features Affine transformation Set-based registration 

References

  1. 1.
    Reilly V, Idrees H, Shah M (2010) Detection and tracking of large number of targets in wide area surveillance. In: European Conference on Computer Vision (ECCV), Crete, Greece 2010Google Scholar
  2. 2.
    Malagi VP, Rangarajan K (2016) Multi-object tracking in aerial Image Sequences using aerial tracking learning and detection algorithm. Defence Sci J 66(2): 122–129Google Scholar
  3. 3.
    Szeliski R (2010) Computer vision: algorithms and applications. Springer Science & Business MediaGoogle Scholar
  4. 4.
    Bhat S, Ramesh Babu DR, Rangarajan K, Ramakrishna KA (2014) Evaluation of feature descriptors to recover camera parameters for navigation of unmanned air vehicles. In: Proceedings of 2nd International Conference on Emerging Research in Computing Information, Communication and Applications, ERCICA-14, 2014, Elsevier Publications. ISBN: 9789351072638Google Scholar
  5. 5.
    Ali S, Shah M (2006) COCOA - Tracking in aerial imagery, SPIE airborne intelligence, surveillance, reconnaissance (ISR) systems and applications, Orlando 2006Google Scholar
  6. 6.
    Arandjelovic O, Pham DS, Venkatesh S (2015) Groupwise registration of aerial images. arXiv preprint. arXiv:1504.05299
  7. 7.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110Google Scholar
  8. 8.
    Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2006Google Scholar
  9. 9.
    Takacs G, et al (2010) Unified real-time tracking and recognition with rotation-invariant fast features. In: 2010 IEEE Conference on IEEE Computer Vision and Pattern Recognition (CVPR), 2010Google Scholar
  10. 10.
    Takacs G, et al (2013) Rotation-invariant fast features for large-scale recognition and real-time tracking. Sig Process: Image Commun 28(4):334–344Google Scholar
  11. 11.
    Takacs G, et al (2013) Fast computation of rotation-invariant image features by an approximate radial gradient transform. IEEE Trans Image Process 22(8):2970–2982Google Scholar
  12. 12.
    Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP 1(2):331–340 2009Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer Vision LabDayananda Sagar College of EngineeringBengaluruIndia

Personalised recommendations