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gpu enhanced Real-time video segmentation

Adam Wagner Kevin Forbes. gpu enhanced Real-time video segmentation. Motivation. Take advantage of GPU architecture for highly parallel data-intensive application Enhance image segmentation using Microsoft Kinect IR depth images

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gpu enhanced Real-time video segmentation

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  1. Adam Wagner Kevin Forbes gpu enhanced Real-time video segmentation

  2. Motivation • Take advantage of GPU architecture for highly parallel data-intensive application • Enhance image segmentation using Microsoft Kinect IR depth images • Reduce frame-to-frame segmentation overhead with optical flow and iterative simulated annealing • “Depth-supported real-time video segmentation with the Kinect” • Algorithm uses Potts model and Metropolis method for segmentation on GPU

  3. Implementation No base source code Software frameworks: • OpenCV – image capture, transformations, optical flow • OpenNI – Kinect middleware • CUDA – NVIDIA GPGPU driven architecture Testbed: rcl1.engr.arizona.edu • CPU: Quad-core Intel Xeon 5160, 3.0GHz • GPU: NVIDIA GeForce GTX 480 • 480 CUDA Cores • GDDR5 • Threads/block = 1024 • Shared memory / block = 48KB

  4. Methodology • Primary effort focused on parallelization of segmentation algorithm • Without source, code was written from scratch for CPU, then parallelized • Memory indexing rearranged to improve coalescing of global loads/stores • Much later in semester, some code became available from paper authors • Image divided into thread blocks on GPU • Image data loaded into block shared memory from global memory • Each thread performs state update on a single pixel

  5. Results Input Image: 512 x 384 RGB to HSV Conversion 2000 Metropolis Iterations

  6. Conclusions • Parallelized algorithm shows vast improvement over CPU version • Makes real-time video processing a possibility • Implementation does not match paper • More improvement possible through use of simpler data types • Still more fine tuned memory arrangement • Increase work done by each thread

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