CS223B Homework 1 Results
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Transcript of CS223B Homework 1 Results
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CS223BHomework 1 Results
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Considered 2 Metrics
• Raw score– Number of pixels in error
• Weighted score– Car pixels weighted more heavily than non-car pixels– Range from 50-100– Formula:
40 * (% of correct car pixels)+ 30 * (1.0 - % of false positive pixels)+ 20 * (% of correct non-car pixels)+ 10 * (1.0 - % of false negative pixels)
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Group Performance (Based on Error Pixels)
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3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000
Average Error Pixels
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mb
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ps
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Group Performance (Based on Weighted Score)
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60 65 70 75 80 85 90 95
Weighted Score
Nu
mb
er o
f G
rou
ps
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Best Solutions
• Eric Park, Brian Tran, Joakim Arfvidsson– 3354 error pixels / score 84.3
• Fraser Cameron, Peter Kimball, Mike Vitus– 3447 error pixels / score 77.2
• Simon Berring, Anya Petrovskaya, Daniel Tarlow– 4337 error pixels / score 86.7
• Antoine el Daher– 4518 error pixels / score 87.2
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Eric Park, Brian Tran, Joakim Arfvidsson
• Road detection:– sample road color from just in front of car– flood-fill the road using the sampled color– use the RANSAC to find the edges of the road– blur and threshold image
• Car edges detection:– Canny– normalize edges – extract horizontal and vertical edges from this result– apply pattern matching
• Use perspective to dismiss false positives
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Eric Park, Brian Tran, Joakim Arfvidsson
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Eric Park, Brian Tran, Joakim Arfvidsson
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Eric Park, Brian Tran, Joakim Arfvidsson
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Fraser Cameron, Peter Kimball, Mike Vitus
• Road finder– Prewitt edge convolution and a Hough Transform
• Tail light finder– based on color
• Shadow finder– looks for dark horizontal edges
• Box finder– uses data from the above to generate bounding box
• Pixel classifier– corner finding -> convex hull to trace car edges
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Fraser Cameron, Peter Kimball, Mike Vitus
Road Finder
Taillight Finder
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Fraser Cameron, Peter Kimball, Mike Vitus
Shadow Finder
Box Finder
Pixel Classifier
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Simon Berring, Anya Petrovskaya, Daniel Tarlow
• Ran four classifiers and combined the results using a naive Bayes model:
1. boosted Haar classifier detector
2. color segmentation
3. corner finding
4. road finding
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Simon Berring, Anya Petrovskaya, Daniel Tarlow
Haar Detector
Color Segmentation
CornerFinding
…
NaïveBayesModel
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Antoine el Daher
• Trained several different boosted Haar classifiers:– 2 rear detectors– 1 "far away car" detector– 1 “side cars" detector– 1 "tail light" detector
• Ran a consistency checking phase– Make sure car is in road region at a plausible depth,
eliminate double detections• Ran a refinement phase
– Tighten bounding box around car using "cube" model of car
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Antoine El Daher
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Antoine El Daher
Taillight Mask
Road Detector
End Result