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Finding Body Parts with Vector Processing

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Finding Body Parts with Vector Processing. Cynthia Bruyns. Bryan Feldman. CS 252. Introduction ... 3. Find local maximums. for every pixel replace with max. ... – PowerPoint PPT presentation

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Title: Finding Body Parts with Vector Processing


1
Finding Body Parts with Vector Processing
  • Cynthia Bruyns
  • Bryan Feldman
  • CS 252

2
Introduction
  • Take existing algorithm for tracking human
    motion, speed up by computing on the GPU.
  • Demonstrate that many vision algorithms are prime
    candidates for using vector processing

3
Demo
Results after false candidates have been removed
4
Vision Algorithms
  • Often computationally expensive-searching over
    many pixels for objects at many orientations and
    scales
  • E.g.
  • ((1024x768)pix)x3colorsx12orientationsx5
    scales
  • Very often the case that highly parallizable

5
Limb Finding
  • Goal find candidate limbs
  • Limbs look like long dark rectangles on light
    backgrounds or long light things on dark
    backgrounds

6
Algorithm specifics
  • 1. Convolution with filter
  • convolve using FFT
  • Response indicates how much pixels go from low to
    high intensity
  • Convolve over all three color channels so as to
    not miss red blue of same intensity


7
Algorithm specifics
  • 2. For every pixel location get respconv from
    left and right, put into new matrix resplimb

x
8
Algorithm specifics
  • 3. Find local maximums
  • for every pixel replace with max. of local
    neighbors. If resplimblocMax its a max

.50 .25 .40 .23 .75 .41 .98 .75 .11 .43
.15 .23 .78 .34 .13 .15
resplimb
locMax
9
GPU
  • Its a good choice because each operation is per
    pixel SIMD-like
  • Data stored in texture buffers equivalent to
    local cache
  • Clean instruction set and developing interface
    language to exploit vector operations
  • Justify your gaming habits

10
GPU dataflow model
  • Hardware supports several data types for
    bandwidth optimization, i.e. 32 bit floating
    point, half etc.
  • Data passed to main memory stages via binding

11
Fragment processor has high resource limits
  • 1024 instructions
  • 512 constants or uniform parameters
  • Each constant counts as one instruction
  • 16 texture units
  • Reuse as many times as desired
  • No branching
  • But, can do a lot with condition codes
  • No indexed reads from registers
  • Use texture reads instead
  • No memory writes

12
The algorithm
  • Draw invokes the fragment programs
  • The texture becomes a data structure use two
    for framebuffers to avoid RAW hazzards

13
Results
  • Mask size fixed (22x13) vary image size

(CPU-2.53 GHz P4 GPU Nvidia FX5900)
Additional GPU optimizations possible
14
Results log scale
  • Mask size fixed (22x13) vary image size

252.1 sec
42.7 sec
(CPU-2.53 GHz P4 GPU Nvidia FX5900)
Additional GPU optimizations possible
15
Results
  • Image size fixed (512x512) vary mask size

Varying mask sizes allow for varying limb sizes
on same image
16
Results
17
Comments
  • GPU and image processing are a good match
  • Time to move memory from CPU to GPU is cumbersome
    but can be overcome
  • Non-uniformity of installations, products, exact
    specifications are hearsay

18
Acknowledgements
  • Kenneth Moreland
  • Deva Ramanan
  • Okan Arikan
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