Rear Lights Vehicle Detection for Collision Avoidance Evangelos Skodras George Siogkas Evangelos Dermatas Nikolaos Fakotakis Electrical & Computer Engineering Dept. University of Patras, Patras, Greece

2 University of Patras

3 Why is this system important? University of Patras To warn drivers about an impeding rear-end collision For autonomous vehicles driving in existing road infrastructure

4 Why hasn’t it been solved yet? University of Patras Great variability in vehicle appearance (shape, size, color, pose) Complex outdoor environments, unpredictable interaction between traffic participants Night driving is a challenging scenario Adverse weather and illumination conditions

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6 Previous work University of Patras Approaches 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

7 Proposed System Overview University of Patras

8 Rear Lights Detection University of Patras Fast radial transform Gradient - based interest operator which detects points of high radial symmetry Determines the contribution each pixel makes to the symmetry of pixels around it Loy, G., & Zelinsky, A. (2003). Fast radial symmetry for detecting points of interest. IEEE Trans. on Pattern Analysis and Machine Intelligence, 959–973. RGB -> L*a*b* FRST Otsu’s Thresholding

9 Blooming effect University of Patras The “blooming effect” is caused by the saturation of the bright pixels in CCD cameras with low dynamic range Saturated lights appear as bright spots with a red halo around Original Imagea* plane of L*a*b*Fast Radial Transform

10 Define Candidate Areas University of Patras Horizontal edge detection Morphological lights pairing Aligned in the horizontal axis Morphological similarity is based on the normalized difference of their axis lengths and areas Morphological lights pairing

11 Verification & Distance Estimation University of Patras Symmetry check Mean Absolute Error (MAE) Structural similarity (SSIM) Distance estimation A precise calculation is not feasible An approximation is achieved assuming an average vehicle width and typical camera characteristics The rate of change of the distance is more important than the absolute distance Symmetry check Distance estimation

12 Experimental results University of Patras Database N UMBER OF IMAGES OR FRAMES Detection Rate Detection Rate when Braking Caltech DB (Cars 1999) %- Caltech DB (Cars 2001) %99.2% Lara Urban Sequence %96.3%

13 Results in adverse weather conditions University of Patras

14 Conclusions University of Patras High detection rates and robustness even in adverse illumination and weather conditions The false positives rate can be reduced by narrowing down the ROI or by using the temporal continuity of the data Efficiently tackles the “blooming effect” with the use of the fast radial transform Easily extendable for vehicle detection at night

15 University of Patras Future work Correlate the danger of an impeding collision (vehicle detection and braking recognition) with the level of attention of the driver (gaze estimation).

16 Thank you for your attention!