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Real-time Object Image Tracking Based on Block-Matching Algorithm . ECE 734 Hsiang-Kuo Tang Tai-Hsuan Wu Ying-Tien Lin. Outline. Introduction Motion Tracking Theories Different Motion Analysis (DMA) Method Block-Matching Algorithm (BMA) Implementation Issues Methodology & Optimizations

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real time object image tracking based on block matching algorithm

Real-time Object Image Tracking Based on Block-Matching Algorithm

ECE 734

Hsiang-Kuo Tang

Tai-Hsuan Wu

Ying-Tien Lin

outline
Outline
  • Introduction
  • Motion Tracking Theories
    • Different Motion Analysis (DMA) Method
    • Block-Matching Algorithm (BMA)
  • Implementation Issues
    • Methodology & Optimizations
    • Different Approaches – C++, PLX, ET44M210
  • Demonstration
introduction
Introduction
  • Motivation: There are many commercial applications about motion tracking
    • Robotic Vision
    • Electrical Pet
    • Traffic Monitoring
    • More….
  • Objective: Efficient Implementation in portable embedded system
    • Simple but powerful algorithms
    • Smart optimizations by developers
object tracking algorithm

The motion object

is here!

Object-tracking algorithm
  • Different motion analysis method
    • SAD of consecutive frames
    • A threshold is set to detect the moving
object tracking algorithm5

The size of tracking area

is not the same as the size

of tracking object !

Object-tracking algorithm
  • Disadvantage of DMA method
    • May include covered or covering background
object tracking algorithm6

Using motion estimation

to adjust the size of

tracking area

Object-tracking algorithm
  • Solution: Block-Matching Algorithm (BMA)
    • Using motion vector to compensate the redundant part of tracking area
implementation methodology optimization
Implementation Methodology & Optimization
  • Capture images from I/O device & transfer RGB to YUV values
    • Pre-compute YUV values & save them in ROM
  • Compute SAD values between adjacent frames
    • Parallel Processing as much as possible
  • Compute motion estimation & compensate tracking area
    • Replace full-search with 41SWS/BPD (FS-like sub-sampling)
implementation approaches
Implementation approaches
  • Simulation in C++ program
    • Evaluate the whole algorithm
  • Simulation in PLX
    • Implement some optimizations
  • Realization in ET44M210 micro-controller
    • Find the performance bottlenecks
implementation approaches plx
Implementation approaches - PLX
  • Optimizations: Parallel Processing
    • Absolute value calculation: 4 ops per register

abs8 macro Rd,Rs1,Rs2 // used in SAD, MAD operations

psub.1.u Rtmp1,Rs1,Rs2

psub.1.u Rtmp2,Rs2,Rs1

padd.1.u Rd,Rtmp1,Rtmp2

endm

    • Load & store operation alignment: 4 ops/register mix.4.r Rtmp6,RGB2,RGB1 // fit 4 RGB values in 1 register

mix.4.r Rtmp7,RGB4,RGB3

store.8 Rtmp6,PLCD,0 // plot them in LCD screen

store.8 Rtmp7,PLCD,8

implementation approaches plx11
Implementation approaches - PLX
  • Results: DMA/BMA between two frames
evaluation of et44m210
Evaluation of ET44M210
  • When running at full speed (48MHz), 11 frames per cycle can be achieved.
  • But due to the USB module, the ET44M210 can only run at 24MHz.
  • Lots of instructions must be cost to handle USB transmission, so that the average performance reduced to 0.9 frame per second.