PPT-Fast Background Subtraction using CUDA

Author : myesha-ticknor | Published Date : 2017-01-14

Janaka CDA 6938 What is Background Subtraction Identify foreground pixels Preprocessing step for most vision algorithms Applications Vehicle Speed Computation from

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Fast Background Subtraction using CUDA: Transcript


Janaka CDA 6938 What is Background Subtraction Identify foreground pixels Preprocessing step for most vision algorithms Applications Vehicle Speed Computation from Video Why is it Hard Naïve Method . Owens University of California Davis One Shields Avenue Davis CA 95616 pmuyan jowens ucdavisedu Junyi Xia Sanjiv S Samant University of Florida Gainesville FL 32611 junyixia samant u64258edu Abstract In the medical imaging 64257eld we need fast defo Basically a child CUDA Kernel can be called from within a parent CUDA kernel and then optionally synchronize on the completion of that child CUDA Kernel The parent CUDA kernel can consume the output produced from the child CUDA Kernel all withou t heterogeneous programming. Katia Oleinik. koleinik@bu.edu. Scientific Computing and Visualization. Boston . University. Architecture. NVIDIA Tesla M2070: . Core clock: 1.15GHz . Single instruction . 448 CUDA cores . . Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin (. Negrut. ), North Carolina Charlotte (. Wikinson. Mental Skills. Recognise the size and position of numbers . Count back in ones and tens. Know number facts for all numbers to 20. Subtract multiples of 10 from any number. Partition and recombine numbers (only partition the number to be subtracted). © Dan Negrut, . 2012. UW-Madison. Dan Negrut. Simulation-Based Engineering Lab. Wisconsin Applied Computing Center. Department of Mechanical Engineering. Department of . Electrical and Computer Engineering. Håkon Kvale . Stensland. iAD-lab, Department for Informatics. Basic 3D Graphics Pipeline. Application. Scene Management. Geometry. Rasterization. Pixel Processing. ROP/FBI/Display. Frame. Buffer. Memory. 22 . Outubro. 2007. . Universidade. Federal do Paraná.. Gerald Dalley, . Xiaogang. Wang, and W. Eric L. Grimson. Glasgow Airport. 4 cameras. 9 video clips. 1 for training. 8 for testing. Dataset Description. Håkon Kvale . Stensland. Simula Research Laboratory. PC Graphics Timeline. Challenges. :. Render infinitely complex scenes. And extremely high resolution. In 1/60. th. of one second (60 frames per second). on . Ubuntu. Cuda. download site. . https://developer.nvidia.com/cuda-downloads. $ . sudo. . dpkg. -. i. cuda-repo-ubuntu1404_7.5-18_amd64.deb . $ . sudo. apt-get update . $ . sudo. apt-get install . introduce the use of multiple CUDA streams to overlap memory transfers with kernel computations.. Also introduced is paged-locked memory. 2. Page-locked host memory. (also called pinned host memory). What is CUDA?. Data Parallelism. Host-Device model. Thread execution. Matrix-multiplication . GPU revised!. What is CUDA?. C. ompute . D. evice . U. nified . A. rchitecture. Programming interface to GPU. Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources. Cliff Woolley NVIDIADeveloper Technology GroupGPUCPUGPGPU Revolutionizes ComputingLatency Processor Throughput processorLow Latency or High ThroughputCPUOptimized for low-latency access to cached dat

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