feature developer at autoliv
TRANSCRIPT
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Patrik Leissner
Feature Developer at Autoliv
November 6, 20171 Feature Developer at Autoliv
Copyright Autoliv Inc., All Rights ReservedNovember 6, 2017 Feature Developer at Autoliv2
and prevent ten times as many severe injuries
Every year our products save over 30,000 lives
OUR VISION
Saving more lives
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20000
40000
60000
80000
100000
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140000
160000
2015 Target
Goal
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What Can a Camera System Do?
November 6, 2017 Feature Developer at Autoliv4
Lane detection
Traffic sign recognition Light source tracking
Object detection
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Target Tracking
What we want
− Object position (3D)
− Object velocity (2D)
− Object size (height / width / length)
− Object type (car / truck /pedestrian / cyclist / motorcyclist)
What we have
− Output from classifiers
− Optical flow
− Stereo information
− Ego motion
− Camera calibration
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Detection of Objects
Boosting-based classifiers
Different feature descriptors withincreased complexity
Output given as regions in theimage where objects are detected.
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Optical Flow
Match pixels in two consecutiveframes (horizontal or vertical)
Pixel motion gives
− Translation
− Scale change
Must take ego motion intoconsideration
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Left
Right
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Stereo Detection
Two cameras
Triangulation gives depthinformation
Segmentation to obtain objectsin the world
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Far
Near
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Stereo Detection – Segmentation
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Target Tracking – Difficulties
Lots of objects
Dynamics
− Differ between object types
− Generally unknown
− No initial guess
Measurements
− Missing
− False
Initialize tracks
Execution on embedded system
− Real-time requirements
− Resource constraints
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Movie – Object Detection
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Reflections of Close Collaboration to Industry
Connection of the work to an actual problem and product.
Hands-on experiments with state of the art technology.
Interesting to test algorithms in reality, not only in simulations.
− However, it is not always possible to test on target hardware
September 19, 2017 Name of presentation12