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Why hasn’t it been solved yet?Big cities pose anextremely hardproblem because ofmultiple light sources.Bad weather makesdetection even moredifficult.Night driving is alsoa challengingscenario.Combining all of theabove, even in smallscale, makesdetection almostimpossible.

What has been tried?• Color thresholding in RGB or HSV• Symmetry Detection with Hough or othernovel methods.• Structural Models of Traffic Lights.• Tracking modules to help minimize falsepositive results.• A mixture of the above.

Image Pre-ProcessingStep 4: Blooming Effect ReductionR-G:Y-B:Image filling of the twoimages followed by addition.This reduces the “bloomingeffect” significantly.

Image Pre-ProcessingStep 4: Blooming Effect ReductionR-G:Y-B:Image filling of the twoimages followed by addition.This reduces the “bloomingeffect” significantly.

Image Pre-ProcessingStep 1: RGB to L*a*b*L*:a*:b*:

Image Pre-ProcessingStep 2: Red – Green EnhancementL*:a*:Multiplying L* and a* toenhance only bright redand green regions(exclude trees, roofs, etc.)

Image Pre-ProcessingStep 3: Yellow – Blue EnhancementL*:Multiplying L* and b* toenhance only bright yellow andblue regions(red lights include yellow andgreen lights include some blue)b*:

Image Pre-ProcessingStep 4: Blooming Effect ReductionR-G:Y-B:Image filling of the twoimages followed by addition.This reduces the “bloomingeffect” significantly.

Image Pre-ProcessingStep 4: Blooming Effect ReductionR-G:Y-B:Image filling of the twoimages followed by addition.This reduces the “bloomingeffect” significantly.

Traffic Light Candidate Detection

Traffic Light Candidate DetectionStep 1: FastRadial Transform

Traffic Light Candidate DetectionStep 1: FastRadial Transform

Traffic Light Candidate DetectionStep 2:Maxima/MinimaLocalization

Traffic Light Candidate Detection

Traffic Light Candidate DetectionStep 1: FastRadial Transform

Traffic Light Candidate DetectionStep 1: FastRadial Transform

Traffic Light Candidate DetectionStep 2:Maxima/MinimaLocalization

Traffic Light Candidate VerificationFRAME #1:Spatiotemporalpersistency check:Traffic Lightscandidates for 4consecutive framesget verified.11 11111

Traffic Light Candidate VerificationFRAME #2:Spatiotemporalpersistency check:Traffic Lightscandidates for 4consecutive framesget verified.22 221

Traffic Light Candidate VerificationFRAME #3:Spatiotemporalpersistency check:Traffic Lightscandidates for 4consecutive framesget verified.132113 1

Traffic Light Candidate VerificationFRAME #4:Spatiotemporalpersistency check:Traffic Lightscandidates for 4consecutive framesget verified.2112142

Traffic Light Candidate VerificationFRAME #5:Spatiotemporalpersistency check:Traffic Lightscandidates for 4consecutive framesget verified.2 11235

Traffic Light Candidate VerificationFRAME #6:Spatiotemporalpersistency check:Traffic Lightscandidates for 4consecutive framesget verified.13223461

Experimental EvaluationQuantitative• Normal weather conditions.• City Driving.• Daytime.• Manually Annotated.• 8-bit, 640x480 @ 25fps• 11179 framesQualitative• Rainy Weather• Night Driving• Different cameras.• Different resolutions.• Downloaded off YouTube,OR shot in Greek roads.The quantitative data is coming fromthe Robotics Centre of Mines ParisTech and ispublicly available at:http://www.lara.prd.fr/benchmarks/trafficlightsrecognition

Results in Normal Conditions

Results in Normal Conditions

Results in Rainy Conditions

Results in Night Driving Scenes

Method LimitationsFalse PositivesNight in Big Cities

Conclusions• Proposed method shows good results inboth normal and adverse conditions.• Many false alarms, that could be reduced bysome kind of structural model.• Extremely difficult cases cannot be tackled.• Future work on construction of an annotateddatabase in adverse conditions.• Also, the incorporation of a tracking moduleand a color consistency module.

Bibliography

Thank you for your attention.Questions?