Real-Time Object Segmentation in Image Sequences 


Vol. 18,  No. 4, pp. 173-180, Aug.  2011
10.3745/KIPSTB.2011.18.4.173


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  Abstract

This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

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[IEEE Style]

E. S. Kang and S. H. Yoo, "Real-Time Object Segmentation in Image Sequences," The KIPS Transactions:PartB , vol. 18, no. 4, pp. 173-180, 2011. DOI: 10.3745/KIPSTB.2011.18.4.173.

[ACM Style]

Eui Seon Kang and Seung Hun Yoo. 2011. Real-Time Object Segmentation in Image Sequences. The KIPS Transactions:PartB , 18, 4, (2011), 173-180. DOI: 10.3745/KIPSTB.2011.18.4.173.

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