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Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

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Abstract

A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system. A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously. To overcome the limitation of the local minimum problem with the level set method, a convex relaxation method is applied to the 3D spatiotemporal segmentation model. The relaxed convex model is independent of the initial condition. A primal-dual algorithm is used to improve computational efficiency. Several indoor experiments show the validity of the proposed method.

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Authors and Affiliations

  1. Department of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, 310027, China

    Shi-yan Wang & Hui-min Yu

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  1. Shi-yan Wang
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  2. Hui-min Yu
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Correspondence to Hui-min Yu.

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Project supported by the National Natural Science Foundation of China (No. 60872069) and the National Basic Research Program (973) of China (No. 2012CB316400)

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Wang, Sy., Yu, Hm. Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method. J. Zhejiang Univ. - Sci. C 13, 428–439 (2012). https://doi.org/10.1631/jzus.C1100331

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