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Rear Lights Vehicle Detection for Collision Avoidance

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

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Rear Lights Vehicle Detection for Collision Avoidance

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  1. 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? To warn drivers about an impeding rear-end collision For autonomous vehicles driving in existing road infrastructure University of Patras

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

  5. Previous work • 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 University of Patras

  6. Proposed System Overview University of Patras

  7. Rear Lights Detection • 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 RGB -> L*a*b* FRST Otsu’s Thresholding Loy, G., & Zelinsky, A. (2003). Fast radial symmetry for detecting points of interest. IEEE Trans. on Pattern Analysis and Machine Intelligence, 959–973. University of Patras

  8. Blooming effect • 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 Image a* plane of L*a*b* Fast Radial Transform University of Patras

  9. Define Candidate Areas • 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 • Horizontal edge detection University of Patras

  10. Verification & Distance Estimation • Symmetry check • Mean Absolute Error (MAE) • Structural similarity (SSIM) • Symmetry check • Distance estimation • 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 University of Patras

  11. Experimental results University of Patras

  12. Results in adverse weather conditions University of Patras

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

  14. Future work • Correlate 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 University of Patras

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

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