Accelerate TV-L1 optical flow with edge-based image decomposition and its implementation on mobile phone

Authors:
Botao Wang;Qingxiang Zhu;Hongkai Xiong;Chuanfei Luo
Affiliations:
Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;Corporation Limited
Venue:
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Year:
2011

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Abstract

Variational methods are among the most accurate techniques of optical flow computation. TV-L1 optical flow, which is based on L1-norm data fidelity term and total variation (TV) regularization term, preserves discontinuities in the flow field and also can deal with large displacements. However, the TV-L1 optical flow method is inaccurate near edges and computationally intensive. In this paper, we proposed a technique, called Edge-based Image Decomposition (EID), to improve the accuracy in the edge areas and also accelerate the original TV-L1 method. EID improves the performance by decomposing image into edge regions and flat regions, and also assigns computing power discriminatively. We evaluated our algorithm on Middlebury datasets and proved that by applying EID, 30% of run-time can be saved with no loss in accuracy, and with same run-time, 7% of accuracy can be promoted. In addition, we implemented our EID-enhanced TV-L1 optical flow algorithm on mobile phone with Android operating system. Our application calculates the optical flow field between two images and can be used to generate the disparity map and reconstruct 3D scenes.