Recognition of highway workzones for reliable autonomous driving(IEEE paper)

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3. Contents
 Introduction
 About autonomous car
 Workzone detection and classification
 Experiments and results
 Conclusion
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4. Autonomous car
 What do you mean by Autonomous Car?
The word autonomous is derived from a Greek roots.
Autonomous = autos + nomos
(SELF) (GOVERN)
So, Autonomous car is a vehicle that can drive itself from
one point to another without assistance from a driver; in
other words, with an autopilot system.
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5. Autonomous car
 What is the need of this type of car?
Driver error is the most common cause of traffic accidents.
Great blessing to physically challenged people.
Cell phones in-car ,entertainment systems, more traffic and
more complicated road systems making it more frequent.
By this improving technology our car will do the
concentrating for us.
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6. Autonomous car
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7. Challenges
Unexpected road
conditions(workzones)
Snowy conditions
 Traffic signal detection
 Legal issues
 High cost of manufacturing
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8. WORKZONE DETECTION
Aims at identifying bounds of a
work zone
Uses computer vision algorithms,
that could recognize work zone
traffic signs.
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9. WORKZONE DETECTION
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10. Recognizing temporary
highway changes
I. SIGN DETECTION
II.SIGN TRACKING
III.SIGN CLASSIFFICATION
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11. I.SIGN DETECTION
TWO METHODS:
Color based
methods
Shape
based
methods
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13. Colour Based detection
 Use color- value thresholding to detect the
potential signs in an image
 Uses machine learning techniques to obtain
the optimal thresholds of target color
 Performs binary pixel wise
classification(Orange or Not Orange)
 Performs non maximal supression
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15. Recognizing temporary
highway changes
I. SIGN DETECTION
II.SIGN TRACKING
III.SIGN CLASSIFFICATION
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16. SIGN TRACKING
 Improves the accuracy of sign detection
 Uses mean shift algorithm on the input
image and identifies potential sign regions
at multiple sub regions.
 Represents different image sub regions as
candidate pdf.
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17. SIGN TRACKING
 Compares the candidate pdf with target pdf
and the candidate pdf with highest score is
chosen
 The output of the tracker is used as the
target pdf for next image.
 The output of the tracker is then given to
the classifier, which classifies the sign.
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19. Recognizing temporary
highway changes
I. SIGN DETECTION
II.SIGN TRACKING
III.SIGN CLASSIFFICATION
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20. SIGN CLASSIFFICATION
 The output from tracker is given as the input to
the sign classification module.
 Removes the background.
 Takes the log polar transform of the input
image.
 A predefined set of nine commonly used signs.
 Compares with predefined set and correctly
classifies the sign image.
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Summarizing

22. EXPERIMENTS & RESULTS
 Collected several hours of video footage of
various driving experiences
 Six videos were selected from them, that
contained workzones
 Detailed analysis was done results of
recognition
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24. EXPERIMENT
Aims to demonstrate how this
recognition system works effectively as
a part of self driving car.
A mock workzone is set up at the
testing site
100m long workzone in 400m long
road.
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26. This test shows how a self driving
car could:
1)recognise the workzone
2)lower its speed
2)drive at normal speed after passing
through the workzone.
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28. Conclusion
 Evaluated some computer vision method
used to localise,detect and classify
workzone.
 Tested this system using several hours of
video footages.
 Results were found to be satisfying.
 This work can be further extended to
recognize and respond to road accidents.
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29. References
 1. J. Lee, Y.-W. Seo, and D. Wettergreen, “Kernel-based
tracking for improving sign detection performance,” in
Proc. IEEE/RSJ Int. Conf. IROS.nov 2013
 2. L. D. Lopez and O. Fuentes, “Color-based road sign
detection and tracking,”in Proc. ICIAR, 2007, pp. 1138–
1147.
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