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Design and Implementation of Optical Flow Estimator for Moving Object Detection in Advanced Driver Assistance System
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 Title & Authors
Design and Implementation of Optical Flow Estimator for Moving Object Detection in Advanced Driver Assistance System
Yoon, Kyung-Han; Jung, Yong-Chul; Cho, Jae-Chan; Jung, Yunho;
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 Abstract
In this paper, the design and implementation results of the optical flow estimator (OFE) for moving object detection (MOD) in advanced driver assistance system (ADAS). In the proposed design, Brox`s algorithm with global optimization is considered, which shows the high performance in the vehicle environment. In addition, Cholesky factorization is applied to solve Euler-Lagrange equation in Brox`s algorithm. Also, shift register bank is incorporated to reduce memory access rate. The proposed optical flow estimator was designed with Verilog-HDL, and FPGA board was used for the real-time verification. Implementation results show that the proposed optical flow estimator includes the logic slices of 40.4K, 155 DSP48s, and block memory of 11,290Kbits.
 Keywords
Advanced driver assistance system;Moving object detection;Optical flow estimation;
 Language
Korean
 Cited by
 References
1.
D. Cheda, D. Ponsa, "Camera egomotion estimation in the ADAS context," in IEEE Conference on Intelligent Transportation System, Funchal: Portugal, pp.1415-1420, Sep. 2010.

2.
Z. Chaohui, D. Xiaohui, X. shuoyu, S. Zheng, "An improved moving object detection algorithm based on frame difference and edge detection," in Fourth International Conference on Image and Graphics, Sichuan: China, pp. 519-523, Aug. 2007.

3.
C. Stauffer, W. E. L Grimson, "Adaptive background mixture models for real-time tracking," in IEEE Computer Society Conference on CVPR, Fort Collins: CO, Vol. 2, pp. 246-252, Jun. 1999.

4.
B. Lucas, T. Kanade, "An iterative image restoration technique with an application in stereo vision," in Proceedings of International Joint Conference on Artificial Intelligence, Vancouver: Canada, Vol. 81, pp. 674-679, Aug. 1981.

5.
B. Horn, B. Schunk, "Determining optical flow," in Technical Symposium East of International Society for Optics and Photonics, Washington: D.C., pp. 185-203, Nov. 1981.

6.
C. Schnorr, "Segmentation of visual motion by minimizing convex non-quadratic functionals," in Pattern Recognition, Conference A: Computer Vision and Image Processing, Proceedings of the 12th IAPR International Conference, Jerusalem: Israel, Vol. 1, pp. 661-663, Oct. 1994.

7.
R. Deriche, P. Kornprobst, G. Aubert, "Optical-flow estimation while preserving its discontinuities: A variational approach," in Second Asian Conference on Computer Vision, Singapore: Singapore, pp. 69-80, Dec. 1995.

8.
L. A. Leon, J. E. Monreal, M. Lefebure, J. S. Perez, "A PDE model for computing the optical flow," Proceedings of Congreso de Ecuaciones Diferenciales y Aplicaciones XVI, Gran Canaria: Spain, pp. 1349-1356, Sep. 1999.

9.
H. Oh, H. Lee, and J. H. Baek, “Moving Object Tracking in UAV Video using Moving Estimation,” Journal of Advanced Navigation Technology, Vol. 10, No. 4, pp. 400-405, Dec. 2006.

10.
D. Kim, S. Rho, and E. Hwang, “Real-time Multi-Objects Recognition and Tracking Scheme,” Journal of Advanced Navigation Technology, Vol. 16, No. 2, pp. 386-398, Apr. 2012. crossref(new window)

11.
T. Brox, Andres Bruhn, Nils Papenberg, Joachim Wickert, "High accuracy optical flow estimation based on a theory for warping," in 8th European Conference on Computer Vision, Prague: Czech Republic, pp. 25-36, 2004.

12.
O. P. Agrawal, "Formulation of Euler-Lagrange equations for fractional variational problems," Journal of Mathematical Analysis and Applications, Vol. 272, pp. 368-379, Aug. 2002. crossref(new window)

13.
R. J. LeVeque, Finite difference methods for ordinary and partial differential equations, Society for Industrial and Applied Mathematics, pp.35-46, 2007.

14.
J. Kim, C. Park, and Y. Lee, "A new nonlinear method for optical flow estimation," The Journal of Korean Institute of Communications and Information Sciences, Vol. 26, No. 4, pp. 463-470, Apr. 2001.