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Real-time on-board object tracking for cooperative flight control

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of possible situations for cooperative flights could be a scenario when the decision on a new path is taken by a certain fleet member, who is called the leader. The update on the new path is transmitted to the fleet members via communication that can be noisy. An optical sensor can be used as a back-up for re-estimating the path parameters based on visual information. For a certain topology, the problem can be solved by continuous tracking of the leader of the fleet in the video sequence and re-adjusting parameters of the flight, accordingly. To solve such a problem a real time system has been developed for recognizing and tracking 3D objects. Any change in the 3D position of the leading object is determined by the on-board system and adjustments of the speed, pitch, yaw and roll angles are made to sustain the topology. Given a 2D image acquired by an on-board camera, the system has to perform the background subtraction, recognize the object, track it and evaluate the relative rotation, scale and translation of the object. In this paper, a comparative study of different algorithms is carried out based on time and accuracy constraints. The solution for 3D pose estimation is provided based on the system of Zernike invariant moments. The candidate techniques solving the complete set of procedures have been implemented on Texas Instrument TMS320DM642 EVM board. It is shown that 14 frames per second can be processed; that supports the real time implementation of the tracking system with the reasonable accuracy.
Słowa kluczowe
Czasopismo
Rocznik
Strony
15--22
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
autor
  • Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 Maryland Parkway, Box 454026 Las Vegas, NV 89154-4026, regent@ee.unlv.edu
Bibliografia
  • [1] Dudani S.A., Breeding K.J., McGhee R.B., Aircraft identification by moment invariants, IEEE Trans. Computer., C-26, 1977, 39-46.
  • [2] Breitenstein M.D., Kuettel D., Weise T., van Gool L., Pfister H., Real-Time Face Pose Estimation from Single Range Images, IEEE Conference on Computer Vision and Pattern Recognition (CVPR’08).
  • [3] Osadchy M., Synergistic face detection and pose estimation with energy-based models, Journal of MachineLearning Research, Vol. 8, 2007, 1197-1215.
  • [4] Yuan C., Niemann H., Object Localization in 2D Images based on Kohonen’s Self-Organization Feature Map, IJCNN99 International Joint Conference on Neural Networks Proceedings, Vol. 5, 1999.
  • [5] Chang K.Y., Ghosh J., Three-Dimensional Model-Based Object Recognition and Pose Estimation Using Probabilistic Principal Surfaces, SPIE: Applications of Artificial Neural Networks in Image Processing V, January 2000, 192-203.
  • [6] Mcivor A.M., Background Subtraction techniques, IVCNZOO, Hamilton, New Zealand, November 2000.
  • [7] Zhiqiang Hou, Chongzhao, A Background Reconstruction Algorithm based on Pixel Intensity Classification in Remote Video Surveillance System, National Key Fundamental Research (973) Program in China.
  • [8] Piccardi M., Background subtraction techniques: a review, IEEE International Conference, Vol. 4, SMC 2004, 3099-3104.
  • [9] McFarlane N., Schofield C., Segmentation and tracking of piglets in images, Machine Vision and Applications, 1995, 187-193.
  • [10] Friedman N., Russell S., Image segmentation in video sequences: A probabilistic approach, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), Morgan Kaufmann Publishers, Inc., San Francisco, CA, 1997, 175-181.
  • [11] Teague M.R., Image analysis via the general theory of moments, J. Opt. Soc. Am., 70, 1980, 920-930.
  • [12] Lowe D.G., Object recognition from local scale-invariant features, International Conference on Computer Vision, Corfu Greece, 1999, 1150-1157.
  • [13] Morel J.M., Yu G., ASIFT: A New Framework for Fully Affine Invariant Image Comparison, SIAM Journal on Imaging Sciences, Vol. 2, Issue 2, 2009.
  • [14] Jeong M.H., You B.J., Lee W.H., Color region tracking against brightness changes, Proc. Australian Joint Conference on Artificial Intelligence, Hobart, Australia, Vol. 4304, December 2006, 536-545
  • [15] Wang J., Yagi Y., Integrating shape and color features for adaptive real-time object tracking, Proc. International Conference on Robotics and Biometrics, Kunming, China, December 2006, 1-6.
  • [16] Comaniciu D., Meer P., Mean shift: A robust approach toward feature space analysis, PAMI, 24(5), 2002, 603-619
  • [17] Zivkovic Z., Krose B., An EM-like algorithm for color-histogram-based object tracking, IEEE Conference on Computer Vision and Pattern Recognition, 2004.
  • [18] Harris C., Stephens M.J., A combined corner and edge detector, Alvey Vision Conference, 1988, 147-152.
  • [19] Kilthau S.L., Drew M.S., Moller T., Full search content independent block matching based on the fast Fourier transform, in: Proceedings of IEEE International Conference on Image Processing, 2002, 669-672.
  • [20] Texas Instruments, TMS320DM642 Evaluation Module Reference Technical, August 2003.
  • [21] Dudani S.A., Moment methods for the identification of three-dimensional objects from optical images, M.Sc. thesis, Ohio State Univ., Columbus, OH, 1971.
  • [22] Mukundan R., Ramakrishnan K.R., Moment Functions in Image Analysis - Theory and Application, World Scientific, Singapore, 1998.
  • [23] Cutler R., Davis L., View-based detection, Proceedings Fourteenth International Conference on Pattern Recognition, Brisbane, Australia, 1, Aug. 1998, 495-500.
  • [24] Cucchiara R., Piccardi M., Prati A., Detecting moving objects, ghosts, and shadows in video streams, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 October 2003, 1337-1342.
  • [25] Gloyer B., H Aghajan., Siu K.Y., Kailath T., Video-based freeway monitoring system using recursive vehicle tracking, Proceedings of SPIE, 2421, February 1995, 173-180.
  • [26] Zhou Q., Agarwal J., Tracking and classifying moving objects from videos, Proceedings of IEEE Workshop on Performance Evaluation of Tracking and Surveillance, 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BATD-0001-0050
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