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

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 Process- ing 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 x 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 x 480 pixels.