A high-speed multi-GPU implementation of bottom-up attention using CUDA

Authors:
Tingting Xu;Thomas Pototschnig;Kolja Kühnlenz;Martin Buss
Affiliations:
Institute of Automatic Control Engineering, LSR, Technische Universität München, München, Germany;Institute of Automatic Control Engineering, LSR, Technische Universität München, München, Germany;Institute of Automatic Control Engineering, LSR, Technische Universität München, München, Germany;Institute of Automatic Control Engineering, LSR, Technische Universität München, München, Germany
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Abstract

In this paper a novel implementation of the saliency map model on a multi-GPU platform using CUDA technology is presented. The saliency map model is a well-known computational model for bottom-up attention selection and serves as a basis of many attention control strategies of cognitive vision systems. A real-time implementation is the prerequisite of an application of bottom-up attention on mobile robots and vehicles. Parallel computation on Graphics Processing Unit (GPU) provides an excellent solution for this kind of compute-intensive image processing. Running on 1 to 4 NVIDIA GeForce 8800 (GTX) graphics cards a frame rate of 313 fps at resolution of 640 × 480 is achieved, which is approximately 8.5 times faster than the standard implementations on CPUs. The implementation is also evaluated using a high-speed camera at 200 Hz. Using two GPUs only 2 ms extra computational time for the saliency map generation in addition to the camera capture time is required for images of 640 × 480 pixels.