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