Optical flow based system for detection of dynamic objects for uavs

Authors

  • A. Molchanov National Aerospace University “Kharkiv Aviation Institute”
  • V. Kortunov V National Aerospace University “Kharkiv Aviation Institute”

DOI:

https://doi.org/10.18372/2073-4751.1.12793

Keywords:

UAV, autopilot, optical flow, least squares method, the sum of absolute differences, detection of objects, avoidance of obstacles

Abstract

This work presents an optical flow based method for obstacle detection by using a single CCDcamera. Computed optical flow is used to detect dynamic obstacles in front of the camera and toadjust rotor's control to avoid them. The proposed system is based on optical flow estimation with weighted image blocks from the streamed video. Hardware simulation is performed to prove theapplicability of this system. Methods and algorithms described in this paper are versatile enough andcan be implemented for various vehicles with autonomous navigation system. The feasibility of theproposed system for UAVs is discussed

Author Biography

V. Kortunov V, National Aerospace University “Kharkiv Aviation Institute”

Doctor of Sciences

References

P. Chalimbaud, F. Berry, F. Marmoiton, and S. Alizon. Design of a hybrid visuoinertial smart sensor. In Proc. Workshop Integration Vision Inertial Sensors (in conjunction with IEEE Int. Conf. Robotics Automation), 2005.

X. Armangue, H. Araujo, and J. Salvi. Differential epipolar constraint in mobile robot egomotion estimation. In Proc. IEEE Int. Conf. Pattern Recognition, pages 599–602, 2002.

Q. Ke and T. Kanade. Transforming camera geometry to a virtual downwardlooking camera: Robust egomotion estimation and ground-layer detection. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pages I–390– I–397, 2003.

M. Clauss, P. Bayerl, and H. Neumann. Segmentation of independently moving objects using a maximum-likelihood principle. In Proc. Autonome Mobile Systeme, 2005.

F. Woelk and R. Koch. Fast monocular Bayesian detection of independently moving objects by a moving observer. In Proc. DAGM Symposium, 2004.

B. K. P. Horn and B. G. Schunck, “Determining optical flow: a retrospective,” Artificial Intelligence, vol. 59, no. 1–2, pp. 81–87, 1993.

Molchanov A.A., Kortunov V.I. Review of motion parameters estimation methods from optical flow [in Russian]. Radioelectronic and computer systems, National aerospace university «Kharkov aviation insitute» named after M.E. Zhukovskiy, Ukraine, 2013(2), pp. 80-85.

Molchanov A.A., Kortunov V.I. Optical flow motion estimation method based on weighted imaging unit measurement [in Russian]. Information processing systems, Kharkiv University of Air Force University named after Ivan Kozhedub, Ukraine, 2015(1), pp. 26-31.

Downloads

Published

2017-05-22

How to Cite

Molchanov, A., & Kortunov V, V. (2017). Optical flow based system for detection of dynamic objects for uavs. Problems of Informatization and Management, 1(57-58), 52–57. https://doi.org/10.18372/2073-4751.1.12793

Issue

Vol. 1 No. 57-58 (2017)

Section

Статті

License

The scientific journal adheres to the principles of Open Access and provides free, immediate, and permanent access to all published materials without financial, technical, or legal barriers for readers.

All articles are published in Open Access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Copyright

Authors who publish their works in the journal:

  • retain the copyright to their publications;

  • grant the journal the right of first publication of the article;

  • agree to the distribution of their materials under the CC BY 4.0 license;

  • have the right to reuse, archive, and distribute their works (including in institutional and subject repositories), provided that proper reference is made to the original publication in the journal.