Browse > Article

Robust Optical Flow Detection Using 2D Histogram with Variable Resolution  

CHON Jaechoon (동경대학 토목공학과)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.23, no.1, 2005 , pp. 49-57 More about this Journal
Abstract
The proposed algorithm is to achieve the robust optical flow detection which is applicable for the case that the outlier rate is over 80%. If the outlier rate of optical flows is over 30%, the discrimination between the inliers and outlier with the conventional algorithm is very difficult. The proposed algorithm is to overcome such difficulty with three steps of grouping algorithm; 1) constructing the 2D histogram with two axies of the lengths and the directions of optical flows. 2) sorting the number of optical flows in each bin of the two-dimensional histogram in the descending order and removing some bins with lower number of optical flows than threshold. 3) increasing the resolution of the two-dimensional histogram if the number of optical flows in a specific bin is over 20% and decreasing the resolution if the number of optical flows is less than 10%. Such processing is repeated until the number of optical flows falls into the range of 10%-20% in all the bins. The proposed algorithm works well on the different kinds of images with many of wrong optical flows. Experimental results are included.
Keywords
Optical flow; Outlier; Inlier; Moving objects; Tracking;
Citations & Related Records

Times Cited By SCOPUS : 0
연도 인용수 순위
  • Reference
1 이진덕, 최용진 (2001), CCTV 유형 CCD 카메라를 이용한 근거리 산업사진측량의 정확도, 한국측량학회지, 제 19권, 제 3호, pp. 283-290
2 Adelson, E. H. and J. R. Bergen, 1985, Spatiotemporal energy models for the perception of motion, Journal of Optical Society of America, A, Vol. 2, No.2, pp. 284-299   DOI
3 Harris, G., 1987, Determination of Ego-Motion From Matched Points, Proc. Alvey Vision Conf, Cambridge UK
4 Rousseeuw, P. J., 1984, Least median of squares regression. Journal of American Statistics Association, 79:871-880   DOI   ScienceOn
5 Smith, S.M. and J. M. Brady, 1995, Real-Time Motion Segmentation and Shape Tracking, IEEE Tr. on PAMI, Vol. 17, No.8
6 Smith, S.M., and J.M. Brady, 1997. SUSAN - a new approach to low level image processing. In IJCV, 23(1), pp. 45-78   DOI   ScienceOn
7 Haralick, R.M. et al., 1988, Pose estimation from corresponding point data. IEEE Tr. SMC, 19(6):1426-1446, Nov
8 장호식, 서동주, 이종출 (2003), 비데오 영상을 이용한 석조 문화재 위치해석, 한국측량학회지, 제 21권, 제 4호, pp. 355-363
9 Horn BKP, Schunck BG, 1981, Determining Optical Flow, Artificial Intelligence 1981, pp. 185-203
10 Kearney, J. K., W. B. Thompson, and D. L. Boley, 1987, Optical flow estimation: An error analysis of Gradient-based methods with local optimization, IEEE Tr. on PAMI, Vol. 9, No.2, pp. 229-244
11 Mokhatarianm, Farzin, and Riku Suomela, 1998, Robust Image Comer Detection Through Curvature Scale Space, IEEE Tr. on PAMI, Vol. 12
12 Kitchen, L., and A. Rosenfeld, 1982, Gray Level Corner Detection, Pattern Recognition Letters, pp. 95-102