Motion Field Estimation Using U-disparity Map and Forward-Backward Error Removal in Vehicle Environment 


Vol. 40,  No. 12, pp. 2343-2352, Dec.  2015


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  Abstract

In this paper, we propose novel motion field estimation method using U-disparity map and forward-backward error removal in vehicles environment. Generally, in an image obtained from a camera attached in a vehicle, a motion vector occurs according to the movement of the vehicle. but this motion vector is less accurate by effect of surrounding environment. In particular, it is difficult to extract an accurate motion vector because of adjacent pixels which are similar each other on the road surface. Therefore, proposed method removes road surface by using U-disparity map and performs optical flow about remaining portion. forward-backward error removal method is used to improve the accuracy of the motion vector. Finally, we predict motion of the vehicle by applying RANSAC(RANdom SAmple Consensus) from acquired motion vector and then generate motion field. Through experimental results, we show that the proposed algorithm performs better than old schemes.

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  Cite this article

[IEEE Style]

S. Seo, G. Lee, S. Lee, J. Yoo, "Motion Field Estimation Using U-disparity Map and Forward-Backward Error Removal in Vehicle Environment," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 12, pp. 2343-2352, 2015. DOI: .

[ACM Style]

Seungwoo Seo, Gyucheol Lee, Sangyong Lee, and Jisang Yoo. 2015. Motion Field Estimation Using U-disparity Map and Forward-Backward Error Removal in Vehicle Environment. The Journal of Korean Institute of Communications and Information Sciences, 40, 12, (2015), 2343-2352. DOI: .

[KICS Style]

Seungwoo Seo, Gyucheol Lee, Sangyong Lee, Jisang Yoo, "Motion Field Estimation Using U-disparity Map and Forward-Backward Error Removal in Vehicle Environment," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 12, pp. 2343-2352, 12. 2015.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880