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In-Car Video Stabilization using Focus of Expansion

Kim, Jin-Hyun    (Department of Electronics and Computer Engineering, Hanyang Univ.   ); Baek, Yeul-Min    (Department of Electronics and Computer Engineering, Hanyang Univ.   ); Yun, Jea-Ho    (Hyundai Mobis Co., Ltd.   ); Kim, Whoi-Yul    (Department of Electronics and Computer Engineering, Hanyang Univ.  );
  • 초록

    Video stabilization is a very important step for vision based applications in the vehicular technology because the accuracy of these applications such as obstacle distance estimation, lane detection and tracking can be affected by bumpy roads and oscillation of vehicle. Conventional methods suffer from either the zooming effect which caused by a camera movement or some motion of surrounding vehicles. In order to overcome this problem, we propose a novel video stabilization method using FOE(Focus of Expansion). When a vehicle moves, optical flow diffuses from the FOE and the FOE is equal to an epipole. If a vehicle moves with vibration, the position of the epipole in the two consecutive frames is changed by oscillation of the vehicle. Therefore, we carry out video stabilization using motion vector estimated from the amount of change of the epipoles. Experiment results show that the proposed method is more efficient than conventional methods.


  • 주제어

    In-car Video Stabilization .   Focus of Expansion .   Epipole.  

  • 참고문헌 (8)

    1. L. Bombini, P. Cerri, P. Grisleri, S. Scaffardi, and P. Zani, "An Evaluation of Monocular Image Stabilization Algorithms for Automotive Applications," Proc. IEEE Conference on Intelligent Transportation Systems, pp. 1562-1567, 2006. 
    2. Y.M. Liang, H.R. Tyan, S.L. Chang, H.Y.M. Liao, and S.W. Chen, "Video Stabilization for a Camcorder Mounted on a Moving Vehicle," IEEE Transactions on Vehicular Technology, Vol.53, Issue 6, pp. 1636-1648, 2004. 
    3. Y. Zhang, M. Xie, and D. Tang, "A Central Sub-image Based Global Motion Estimation Method for In-Car Video Stabilization," Proc. Third International Conference on Knowledge Discovery and Data Mining, Phuket, Thailand, pp. 204-207, 2010. 
    4. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, second edition, 2004. 
    5. C. Tomasi and J. Shi, "Good Features to Track," Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593-600, 1994. 
    6. J.Y. Bouguet, "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of The algorithm," Intel Corporation, Microprocessor Research Labs, OpenCV Documents, 2000. 
    7. J.K. Lee and C.J. Park, "Algorithm for Arbitrary Point Tracking using Pyramidal Optical Flow," J . of Korea Multimedia Society, Vol.10, No.11, pp. 1407-1416, 2007.     
    8. G. Welch and G. Bishop, "An introduction to the kalman filter," in SIGGRAPH, Course 8, 2001. 

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