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Real-Time Online Video Object Silhouette Extraction Using Graph Cuts on the GPU

Zachary A. Garrett, Hideo Saito
Department of Information and Computer Science, Keio University, 3-4-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522 JAPAN
Image Analysis and Processing – ICIAP 2009, Lecture Notes in Computer Science, 2009, Volume 5716/2009, 985-994

@article{garrett2009real,

   title={Real-Time Online Video Object Silhouette Extraction Using Graph Cuts on the GPU},

   author={Garrett, Z. and Saito, H.},

   journal={Image Analysis and Processing–ICIAP 2009},

   pages={985–994},

   year={2009},

   publisher={Springer}

}

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Being able to find the silhouette of an object is a very important front-end processing step for many high-level computer vision techniques, such as Shape-from-Silhouette 3D reconstruction methods, object shape tracking, and pose estimation. Graph cuts have been proposed as a method for finding very accurate silhouettes which can be used as input to such high level techniques, but graph cuts are notoriously computation intensive and slow. Leading CPU implementations can extract a silhouette from a single QVGA image in 100 milliseconds, with performance dramatically decreasing with increased resolution. Recent GPU implementations have been able to achieve performance of 6 milliseconds per image by exploiting the intrinsic properties of the lattice graphs and the hardware model of the GPU. However, these methods are restricted to a subclass of lattice graphs and are not generally applicable. We propose a novel method for graph cuts on the GPU which places no limits on graph configuration and which is able to achieve comparable real-time performance in online video processing scenarios.
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