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.

CS223BHomework 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– 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)

Group Performance (Based on Error Pixels)

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Average Error Pixels

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Group Performance (Based on Weighted Score)

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Weighted Score

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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

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




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


Road Finder

Taillight Finder


Shadow Finder

Box Finder

Pixel Classifier

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

Simon Berring, Anya Petrovskaya, Daniel Tarlow

Haar Detector

Color Segmentation

CornerFinding

…

NaïveBayesModel

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

Antoine El Daher

Antoine El Daher

Taillight Mask

Road Detector

End Result

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.

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