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Parallelizing OpenCV: Optical Flow Algorithms. Arjun Agarwal Abhishek Gupta Eric Lovett. Optical Flow. Apparent motion of objects, surfaces and edges in a scene caused by the relative motion between scene and observer Use cases: For tracking objects

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parallelizing opencv optical flow algorithms

Parallelizing OpenCV:Optical Flow Algorithms

Arjun Agarwal

Abhishek Gupta

Eric Lovett

optical flow
Optical Flow
  • Apparent motion of objects, surfaces and edges in a scene caused by the relative motion between scene and observer
  • Use cases:
    • For tracking objects
    • determine whether the objects are moving or the observer is moving
  • Three of the ways to do this in OpenCV that are not yet parallelized:
    • Horn & Schunck algorithm (Iterative)
    • Lucas & Kanade algorithm (Iterative)
    • Block matching method (Data decomposition)
what we have done
What we have done
  • Parallelized OpticalFlow BM and LK
  • Methodology:
    • Used OpenMP
    • Changed while loops to for loops (e.g. LK)
    • Removed inter-loop dependencies
    • Used data-driven decomposition
    • Analyze across:
      • Number of processors
      • Data granularity
      • Input size
to do
To Do
  • Need to finish parallelizing OpticalFlowHS
  • Explore why the speedups are not much
    • False Sharing*
    • Figure out what the sequential overhead is doing (matrix manipulations?) and if we can reduce or parallelize it*
    • Problem size is too small (we intend to explore the speedup for high definition videos)
    • We intend to explore wall-clock times for different scheduling (static vs. dynamic)
evaluation
Evaluation
  • Evaluation is done on Niagara-2
    • It has 64 byte cache line
    • It has 8 processors and 8 hardware threads per processor
optical flow lk
Optical Flow LK
  • Inter-loop Dependency
    • Some variables in Iteration i depended on iteration (i-2)
    • Solution: We calculated those variables in the ith using the data that we have at ith iteration
    • We
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