workshop - november 2011 - toulouse (soc toolkit for critical embedded systems) thales use case:...
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Workshop - November 2011 - Toulouse
(SoC toolKit for critical Embedded sysTems)
Thales Use Case:
Pedestrian tracking with smart cameras
SoCKET Collaborative Project
Paul [email protected]
23/11/2011 1
IntroductionThales interest on the project.
SOCKET Global Flow
Thales Use case description: Application description. Create Classifiers. C application. Architecture description.
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Thales Interest
Thales Research has several interests on the project: Interaction between control and dataflow. Predictability real time comportment. Reduce the programming/re-programming cost. Verification.
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SoCKET Flow
Global SoC Req.
SoCArchitecture
Functional validation
SW Performance Validation
C/C++/ASM
Functionality
Timing +Functionalit
y
Instruction Set
Simulator
System Requirements
PlatformAssembly
MetricsHLS
SystemProperties
Hardwareproperties
Softwareproperties
TLMLT
TLMAT
Software
Software
Co-simulation/Co-emulation
Silicon SoftwareExecution
HLS
Trafficgenerator
Metrics
IP-XactSoC
Headersgeneration
RTL SoftwareR
eq
uir
em
en
ts t
raceab
ilit
y
4
Thales use case – ContextContext
Safety of critical infrastructures containing image processing Detection of intrusions pedestrians
Description Environment multi-camera (basic scenario using 2 cameras) having a specific
visual field
Centralized process of the video stream coming from a reduced number of camera in an System on Chip embedded system
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Use case TRT – Description App.Pedestrians detection
Algorithm of classification [Viola&Jones]
Two steps: Off-line: training by an image database On-line: detection by using the training results
Pedestrians tracking in an multi-camera environmentUse of the visual covering of the cameras in order to
carry out the trackingUtilization of descriptors of forms and/or colors in order
to improve the tracking and to manage occlusions
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Thales use case – Appl. descriptionSome examples [Viola&Jones ICCV ’03]
Database
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Thales use case – App. descriptionProcedure
The training is carried out on a workstation The SoC applies the classifiers and the tracking
algorithm on multi-camera Synoptic Application
CameraIntegralImage
Tiler
Features
ClassifiersCascade
Bounding BoxDescriptor
DescriptorTable
ManageDescriptors
Trackingresult
SoC
IO Frontend processing Backend processing
Host
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Thales use case – App. DescriptionSchematic view of the pedestrian detection
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To create the classifiersFrom scratch:
A huge database is needed: Minimum 10 000 positive and negative images to have good
classifiers.
A good workstation and Time: Take about 30 minutes on 8 cores Xeon 2.4GHz and 12Gb
RAM. 2 Weeks on Dual core and 2Gb RAM.
From existing classifiers:Convert to the Thales classifiers by XML parsing.
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C ApplicationClassifiers (off-line) C Code (on-line)
Result
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Thales use Case – ArchitectureHost Architecture details
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Thales use Case – ArchitectureAccelerator details (Engine)
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23/11/2011
Questions?
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