Real Time Motion Detection Based on the Spatio-Temporal Median Filter using GPU Integral Histograms

Abstract

We describe a parallel 3D spatio-temporal median filter algorithm implemented in CUDA for many core Graphics Processing Unit (GPU) architectures using the integral histogram as a building block to support adaptive window sizes. Both 2D and 3D median filters are also widely used in many other computer vision tasks like denoising, segmentation, and recognition. Although fast sequential median algorithms exist, improving performance using parallelization is attractive to reduce the time needed for motion detection in order to support more complex processing in multi-target tracking systems, large high resolution aerial video imagery and 3D volumetric processing. Results show the frame rate of the GPU implementation was 60 times faster than the CPU version for a 1K x 1K image reaching 49 fr/sec and 21 times faster for 512 x 512 frame sizes reaching 194 fr/sec. We characterize performance of the parallel 3D median filter for different image sizes and varying number of histogram bins and show selected results for motion detection.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2012
Accession Number
ADA590600

Entities

People

  • Filiz Bunyak
  • Guna Seetharaman
  • Kannappan Palaniappan
  • Mahdieh Poostchi

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Computations
  • Computer Science
  • Computer Vision
  • Computers
  • Data Transmission
  • Detection
  • Graphics
  • Graphics Processing Unit
  • High Resolution
  • Histograms
  • Integrals
  • Military Research
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Fluid Dynamics (CFD)
  • Computer Programming and Software Development.
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML