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CS223B Homework 1 Results

CS223B Homework 1 Results. 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

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CS223B Homework 1 Results

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  1. CS223BHomework 1 Results

  2. 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)

  3. 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

  4. 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

  5. Eric Park, Brian Tran, Joakim Arfvidsson

  6. Eric Park, Brian Tran, Joakim Arfvidsson

  7. Eric Park, Brian Tran, Joakim Arfvidsson

  8. 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

  9. Fraser Cameron, Peter Kimball, Mike Vitus Road Finder Taillight Finder

  10. Fraser Cameron, Peter Kimball, Mike Vitus Shadow Finder Box Finder Pixel Classifier

  11. Simon Berring, Anya Petrovskaya, Daniel Tarlow • Ran four classifiers and combined the results using a naive Bayes model: • boosted Haar classifier detector • color segmentation • corner finding • road finding

  12. Simon Berring, Anya Petrovskaya, Daniel Tarlow NaïveBayesModel … Haar Detector Color Segmentation CornerFinding

  13. 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

  14. Antoine El Daher

  15. Antoine El Daher Taillight Mask Road Detector End Result

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