Rear Lights Vehicle Detection for Collision AvoidanceEvangelos SkodrasGeorge SiogkasEvangelos DermatasNikolaos FakotakisElectrical & Computer Engineering Dept. University of Patras, Patras, Greece

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*University of Patras

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Why is this system important?University of PatrasTo warn drivers about an impeding rear-end collisionFor autonomous vehicles driving in existing road infrastructure

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Why hasnt it been solved yet?University of PatrasGreat variability in vehicle appearance (shape, size, color, pose)Complex outdoor environments, unpredictable interaction between traffic participantsNight driving is a challenging scenarioAdverse weather and illumination conditions

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

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IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Previous workUniversity of PatrasApproaches using vehicle rear lights

Color thresholding in RGB or YCbCr using mostly empirical thresholds

Color thresholding in HSV with thresholds derived from the color distribution of rear-lamp pixels under real world conditions

In most cases for vehicle detection at night

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Proposed System OverviewUniversity of Patras

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Rear Lights DetectionUniversity of PatrasFast radial transformGradient - based interest operator which detects points of high radial symmetry Determines the contribution each pixel makes to the symmetry of pixels around itLoy, G., & Zelinsky, A. (2003). Fast radial symmetry for detecting points of interest. IEEE Trans. on Pattern Analysis and Machine Intelligence, 959973.RGB -> L*a*b*FRSTOtsus Thresholding

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Blooming effectUniversity of PatrasThe blooming effect is caused by the saturation of the bright pixels in CCD cameras with low dynamic rangeSaturated lights appear as bright spots with a red halo around Original Imagea* plane of L*a*b*Fast Radial Transform

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Define Candidate AreasUniversity of PatrasHorizontal edge detectionMorphological lights pairingAligned in the horizontal axisMorphological similarity is based on the normalized difference of their axis lengths and areasMorphological lights pairing

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Verification & Distance EstimationUniversity of PatrasSymmetry checkMean Absolute Error (MAE)Structural similarity (SSIM)

Distance estimationA precise calculation is not feasibleAn approximation is achieved assuming an average vehicle width and typical camera characteristicsThe rate of change of the distance is more important than the absolute distanceSymmetry checkDistance estimation

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Experimental resultsUniversity of Patras

DatabaseNUMBER OF IMAGES OR FRAMES Detection RateDetection Rate when BrakingCaltech DB(Cars 1999)12692.1%-Caltech DB(Cars 2001)50493.6%99.2%Lara Urban Sequence 1271692.6%96.3%

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Results in adverse weather conditionsUniversity of Patras

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*ConclusionsUniversity of PatrasHigh detection rates and robustness even in adverse illumination and weather conditionsThe false positives rate can be reduced by narrowing down the ROI or by using the temporal continuity of the dataEfficiently tackles the blooming effect with the use of the fast radial transformEasily extendable for vehicle detection at night

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*University of PatrasFuture workCorrelate the danger of an impeding collision (vehicle detection and braking recognition) with the level of attention of the driver (gaze estimation).http://www.youtube.com/watch?v=YyLfpNA2f5U

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Thank you for your attention!evskodras@upatras.gr

IEEE-IECON 2009, November 3-5, 2009, Porto, Portugal

*Enhancing driving safety has attracted a lot of attention lately, following the dire statistics in terms of expenses and human casualties. Although vehicle safety improvement has significantly decreased the death toll and injuries in vehicle crashes, accident prediction and prevention is the maximizing driving safety. Robust and reliable vehicle detection is a critical step**Up to now existing state-of-the-art systems implemented by the automotive manufacturers account on active sensors (e.g. radar based or laser based). ***Divided into three stages**Until now it is handled using hardware approaches (high dynamic cameras or special filters)*Assuming that the vehicle is in the same tilt the candidate light pairs must be aligned in the horizontal axis (with a permissible inclination of 5 degrees)*Symmetry is one of the main signatures of the man made objects. Vehicles observed from the rear are symmetrical in the vertical direction

MAE: straightforward and efficientSSIM: similarity using three comparisons regarding luminance, contrast and structure

As a single frame cannot contain enough information

*High d*Our system was also tested on images aquired under adverse weather conditions, downloaded from the internet. As long as the rear lights are visible the system performs sufficiently well**also independent of the camera used Because no static thresholds are usedWith the incorporation of a tracking algorithm***