Team 11 Senior Project CS 426 Spring...
Transcript of Team 11 Senior Project CS 426 Spring...
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Team 11
Senior Project CS 426
Spring 2012
Students: Issa Beekun Joshua Gleason Marvin Smith Steven Wood
External Advisers: Dr. George Bebis, UNR CSE Department Kurt Dietrich, Associate Civil Engineer, Traffic Engineering Division, City of Reno
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Core: • Detect any vehicles in image regardless of external
conditions • Accurately count vehicles passing through
intersections regardless of external conditions
Potential: • Generic classification based on type of vehicle
(truck, sedan, sports utility vehicle) • Discrimination between vehicles, pedestrians,
cyclists • Behavioral analysis (vehicles improperly parked in
emergency zones, suspicious activity, etc.)
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First came timers...
…followed by inductive loop sensors to detect if vehicle present
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Loop operation sensitive to roadway conditions
Extremely invasive and expensive installation procedure
And overall, the potential data collection from loop sensors is very limited.
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Easy to install…
Easy to use and maintain…
Existing products with over $100 million in annual sales…
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• Rely on a single color camera as input sensor
• Good performance is limited to Daylight Clear weather
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Traffic monitoring using thermal and
visible light fusion.
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Changes in lighting, occlusion, texture, and orientation greatly affect object recognition algorithms in computer vision.
Humans are much better at pattern recognition with partial data.
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“edge”: no change
along the edge
direction
“corner”: significant
change in all
directions
“flat” region:
no change in all
directions
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Get thermal and visible cameras working in field Find locations that mimic position of traffic camera (overpasses)
Film each site in different external conditions (time of day, weather)
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Each device works automatically and is powered by hard work and determination
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Need monitor to observe output and a source to power it all
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Not getting arrested while recording data
The roof of the van is sufficiently elevated to see over the fence
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In order to evaluate our results, we use an analysis tool which will compute the Precision Recall Curve.
Precision = true positives
true positives + false positives
Recall = true positives
true positives + false negatives
Recall: The likelihood that an object is found. Precision: The likelihood that an object found is accurate.
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Team Website
Git Distributed Version Control
Github Issue and Bug Tracking
Doxygen Code Management
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1. Develop labeled ground truth using Vatic.
2. Finish building segmentation module.
3. Integrate Qt GUI with Vision Module.
4. Achieve an accuracy of 80% with a PR Curve Area of over 0.6.
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http://sherifftechnologies.com/civil-
engineering/groundworks-installation-
civils/loop-cutting-contracts
http://www.fhwa.dot.gov/publications/pu
blicroads/07nov/04.cfm
http://auto.howstuffworks.com/car-
driving-safety/safety-regulatory-
devices/red-light-camera1.htm
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http://ops.fhwa.dot.gov/freewaymgmt/pu
blications/frwy_mgmt_handbook/chapter
15_01.htm
http://www.fhwa.dot.gov/publications/res
earch/operations/its/06139/chapt6.cfm
http://www.nctimes.com/news/local/swco
unty/article_3ac18877-b62c-5a59-a8ce-
e8ab648baffa.html
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• http://www.telegraph.co.uk/news/worldnews/northamerica/usa/7177471/US-east-coast-shut-down-as-snowpocalypse-hits.html
• http://www.instructables.com/id/Trigger-GREEN-Traffic-Lights/
• http://www.richmond.ca/services/ttp/signals/faq.htm
• http://attackoftheblargg.blogspot.com/2011/01/rain-traffic.html
• http://www.nationalgeographicstock.com