Spatiotemporal saliency map of a video sequence in fpga hardware
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Spatiotemporal Saliency Map of a Video Sequence in FPGA hardware. David Boland Acknowledgements: Professor Peter Cheung Mr Yang Liu. What is Spatiotemporal Saliency?. Saliency – parts of a scene that appear pronounced

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Spatiotemporal Saliency Map of a Video Sequence in FPGA hardware

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Spatiotemporal Saliency Map of a Video Sequence in FPGA hardware

David Boland

Acknowledgements:

Professor Peter Cheung

Mr Yang Liu


What is Spatiotemporal Saliency?

  • Saliency – parts of a scene that appear pronounced

  • Spatiotemporal Saliency – parts of a scene that appear pronounced in video


Why Important?

  • General environments are complex and dynamic

  • Human eye handles this by focusing upon salient objects

  • Real-time algorithm to emulate this has many uses:

    • Image processing

    • Surveillance

    • Machine vision

    • Navigation…


The Problem

  • Spatiotemporal Saliency algorithms have high computational complexity.

    • Store stack of video frames

    • Unsuitable for real-time

      • Need algorithm with reduced memory requirements


Overview

  • Introduce Algorithm and section completed

  • Brief background

  • Implementation

    • Software Model

    • Hardware Model

  • Results

  • Optimisations (if time)

  • Summary


Algorithm For Spatiotemporal Saliency


Feature Tracking Module

  • Object tracking generally achieved through monitoring optical flow

  • Optical flow: “the distribution of apparent velocities of movement of brightness patterns in an image”

  • Several Algorithms – None perfect

  • Good Trade complexity vs. accuracy – Lukas Kanade Algorithm


Lukas Kanade Algorithm

  • Definition of problem:

    • Let I and J are two consecutive images

    • Let u = [ux, uy ] be an image point in I

    • Find v = u + d = [ux+dx, uy+dy] where v is a similar point on J

  • Points not tracked equally due to aperture problem.

  • Solution is to minimise error:


Lukas Kanade Solution

(Iteratively Refine)

Find

where


Pyramidal Lukas Kanade Algorithm

  • Lukas Kanade Algorithm assumes small motion

  • Handle Larger motion with window size

    • But Lose Accuracy

  • Solution

    • Create Hierarchy of images

      • Each image ½ as large

    • Perform Lukas Kanade on each level to get guess

    • Map guess to lower levels


Pyramidal Lukas Kanade Algorithm

Map guess to lower levels, obtain better guess

Find final pixel location

Track feature between two images at the highest level to obtain guess for new feature location

Apply LK, start at guess

Apply LK, start at guess

Apply LK


Implementation – Software Model

  • Why?

    • Results to test the hardware against

    • Useful during debugging stage

  • Choice of Software Language: Matlab

    • Matrix calculations

    • Maps well to hardware

    • Simple for fast development

  • Method:

    • Apply feature detection algorithm to find co-ordinates

    • Apply Pyramidal Lukas Kanade to track co-ordinates


Software Model - Demo


Implementation – Hardware

  • Aims:

    • Fit onto the FPGA

    • Clock Frequency 65MHz for VGA

  • Not Straightforward:

    • Initial design emulate software correctly:

      • Well over 200% size of FPGA

      • Initial Design 4MHz


Hardware Considerations

  • Choice Software Language: Handel-C

  • Minimise expensive operations

    • Memory Accesses

    • Multiplication

    • Division

  • Maintain Precision

    • Floating point precision unavailable

  • General Optimisations

    • Minimise Delay Path or Logic Depth

    • Minimise Fan-out


Memory Considerations – Building Hierarchy

  • To build image of higher level:

    • Iterate over even pixels

    • Collect mask of values surrounding the pixel

    • Weight as shown on right

    • Sum

  • Repeat recursively on output for higher levels


Memory Considerations – Building Hierarchy

  • Pixels re-used:

    • Store locally

    • Reduce Memory reads


Memory Considerations – Building Hierarchy


Memory Considerations – Optical Flow

  • Only read once values once from main memory

  • Also reduce fan-out


Multiplications

  • Avoid via left-shifting

  • Pre-compute results whenever possible

  • Use Dedicated Multipliers

    • Combined for large multiplications


Division Considerations

  • Division Costly process

  • Handel-C designs hardware to implement in one cycle.

  • Large number of bits implies large delay

  • Solution: Spread over multiple cycles

    • Long Division

      • Slow – unbounded stage

    • Binary Search

      • If limit range of optical flow per iteration [-1 1]


1 B

0.825B

<

0.75 B

0.75 B

<

0.625B

<

0.5 B

0.5 B

0.375 B

<

<

0.25 B

0.25 B

<

0.125B

<

0 B

Division Considerations

A/B=x≡ A=B*x


Division Considerations

1 B

111

1

0.825B

1

0

0.75 B

0.75 B

110/101

1

1

0

0.625B

0

0.5 B

0.5 B

100/011

1

0.375B

1

0

0

0.25 B

0.25 B

010/001

1

0

0.125B

0

0 B

000


Hardware Testing

  • Test against software model

    • Store Feature co-ordinates & tracked locations from software model

    • Load feature co-ordinates in hardware

    • Track in hardware

    • Compare difference

  • Vary number of fractional bits

    • Examine importance/cost of different fractional precision


Accuracy Results (I)


Accuracy Results (II)


Area Results


Speed Results


Results Summary

  • Final design only uses 1/6 FPGA

  • Use 4/5/6 fractional bits for good accuracy

  • Speed short of desired (approx 50 MHz)

    • ISE estimates cautious

    • Pipelining can increase this

      • Reduced Loop control


Optimisations

  • Final Design only uses 1/6 FPGA.

  • Use space to increase Speed:

    • Pipelined Hardware

    • Parallel Hardware


Pipelined Architecture I


Pipelined Architecture II


Parallel Architecture


Summary

  • Spatiotemporal Saliency framework

  • Role of optical flow within framework

  • Steps to create & test hardware implementation

  • Effective method to find optical flow

    • High Speed/Accuracy, small area

      • Optimisations to achieve this

      • Further Improvements possible

    • Some performance advantages over other hardware optical flow implementations

  • Optical flow useful beyond Spatiotemporal Saliency Framework


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