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Devices Synchronization for modeling « 3D plane » 1 Rémi Devinant DII5 / Devices synchronization for modeling 3D plane
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1Rémi Devinant DII5 / Devices synchronization for modeling 3D plane.

Dec 19, 2015

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Page 1: 1Rémi Devinant DII5 / Devices synchronization for modeling 3D plane.

Rémi Devinant DII5 / Devices synchronization for modeling 3D plane 1

Devices Synchronization for modeling « 3D plane »

Page 2: 1Rémi Devinant DII5 / Devices synchronization for modeling 3D plane.

Rémi Devinant DII5 / Devices synchronization for modeling 3D plane 2

Summary

Introduction Context Problem Modeling Devices Synchronization Computer vision Conclusion

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Introduction

More and more augmented reality applications Development of more performant technologies

Best performances Reliability tools for users

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Context

Smartphones More than 100 applications 2 known techniques▪ Geolocalization and camera▪ Camera, gyro, accelerometer▪ Embedded systems constraints

« EyeTape » prototype Head up display Display information superimposed on vision

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Context

The project Based on a CCD

camera▪ Taking frames in

video▪ 2 kinds of

information to▪ Static (buildings, urban

environment…)▪ Dynamic (people,

cars…)

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Context

Ubiquitous Computing Mobile systems around users Interacts with the environment Devices used in parallel Mobile system▪ Video treatment▪ 2 informations flow

Embedded systems problematic Light mobile system Light CPU power Memory constraints

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Context

Smartphone (iPhone 4) composed of CCD 5mPixels (video 720p à 30fps) Gyro L3G4200D Digital 3-axis Accelerometer ST Micro LIS331DLH 3-axis CPUApple A4 APL0398 (ARM Cortex A8) DMA Memory RAM 512Mo DRAM Samsung

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Context

Devices synchronisation Apple iOS Apple iPhone OS Based on a BSD Kernel Mac OS X with cellphone services

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Context

Video frames overlap Video’s framerate preset Purpose Reduce or increase framerate Original 30fps Smartphone motion in space

Slow motion : video frames can overlap significantly need for suppressing useless ones

Need for fusioning devices datas Allow selection in video frames

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Context

2 known methods Hardware▪ Can exist if it’s built-in▪ Smartphones dont' have this characteristic

Software▪ Can be implemented in the OS▪ Require an personalized algorithm

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Summary

Introduction Context Problem Modeling Devices Synchronization Computer vision Conclusion

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Problem modeling

Each devices have different time delays In retrieving datas In transmitting datas

Require modeling each delays representing the time between the datas are required and the time where they are in memory

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Problem modeling

Basic system architecture

Gyroscope

Accéléromètre

Capteur CCD

Device Controller gyro

Device Controller accéléromètre

Device Controller CCD

Buffer mémoire DMA

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Problem modeling

Accelerometer 3 datas send into 1 block 3 axis acceleration sensor

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Problem modeling

Accéléromètre

DeviceController

Mémoire

CPU

Data BusAdresses BusControl Bus

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Problem modeling

Accelerometer Retrieving datas delay

Devices bus output: fbus Datas volume: Vdata Datas acquisition delay: ∆tacqacc Bus acquisition delay (if busy): delay Writing in memory delay depending on datas

volume : ∆twrite

∆tacc = ∆tacq + (fbus / Vdata) + ∆twrite + delay

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Problem modeling

Gyro 3 datas send into 1 block 3 axis rotations

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Problem modeling

Gyroscope

DeviceController

Mémoire

CPU

Data BusAdresses BusControl Bus

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Problem modeling

Gyro Retrieving datas delay

Devices bus output: fbus Datas volume: Vdata Datas acquisition delay: ∆tacqacc Bus acquisition delay (if busy): delay Writing in memory delay depending on datas

volume : ∆twrite

∆tacc = ∆tacq + (fbus / Vdata) + ∆twrite + delay

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Problem modeling

CCD Camera Treatment of frames

before they are placed in memory

sRGB colors 720p Red/ Green/ Blue Heavy data volume DMA

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Problem modeling

CCD

DeviceController

Mémoire

CPU DMA

Data BusAdresses BusControl Bus

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Problem modeling

CCD Camera Retrieving datas delay

Devices bus output: fbus Datas volume: Vdata Datas acquisition delay: ∆tacqacc Bus acquisition delay (if busy): delay Writing in memory delay depending on datas

volume : ∆twrite

∆tacc = ∆tacq + (fbus / Vdata) + ∆twrite + delay

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Summary

Introduction Context Problem Modeling Devices Synchronization Computer vision Conclusion

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Devices synchronization

T0

T1 ∆t

Recording at 30fps

Windows of N scan in memory for finding frame

Start memory scan window

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Devices synchronization

Frame Spotted at ti

- ti : date of scan

- ∆t : retrieveing image delay

- ti - ∆t = real date of the frame capture

∆t

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Devices synchronization

In this window If a frame is spotted at pi S∆ = pi – (N - 1) / 2

Next window start at T1 + S∆

Window placed every time near the last image was spotted Real time and embedded constraints

context

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Devices synchronization

When the window is started, creation of coordinate table Accelerometer▪ ta[] = tspotted - ∆tacc

Gyro▪ tg[] = tspotted - ∆tgyr

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Devices synchronization

Coordinate are chosed tCoordonnees = tImgSpotted - ∆tImage We try to keep the nearest coordinate

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Summary

Introduction Context Problem Modeling Devices Synchronization Computer vision Conclusion

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Computer Vision

A frame = spatial coordinates Smartphone’s spatial localization

Spatial localization= frame plane Creates frame 3D plane

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Computer Vision

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Computer Vision

Modeling 3D plane