rear lights vehicle detection for collision avoidance evangelos skodras george siogkas evangelos...
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Rear Lights Vehicle Detection for Collision Avoidance
Evangelos Skodras
George Siogkas
Evangelos Dermatas
Nikolaos Fakotakis
Electrical & Computer Engineering Dept. University of Patras, Patras, Greece
2University of PatrasUniversity of Patras
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Why is this system important?
University of PatrasUniversity of Patras
To warn drivers about an impeding rear-end collision
For autonomous vehicles driving in existing road infrastructure
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Why hasn’t it been solved yet?
University of PatrasUniversity 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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Previous work
University of PatrasUniversity 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
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Proposed System Overview
University of PatrasUniversity of Patras
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Rear Lights Detection
University of PatrasUniversity of Patras
Fast radial transformFast radial transformGradient - 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.
RG
B
->
L*a
*b*
RG
B
->
L*a
*b*
FR
ST
FR
ST
Otsu’s ThresholdingOtsu’s Thresholding
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Blooming effect
University of PatrasUniversity 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 Image a* plane of L*a*b* Fast Radial Transform
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Define Candidate Areas
University of PatrasUniversity 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
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Verification & Distance Estimation
University of PatrasUniversity 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
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Experimental results
University of PatrasUniversity of Patras
DatabaseNUMBER OF IMAGES OR
FRAMES Detection Rate
Detection Rate when Braking
Caltech DB(Cars 1999)
126 92.1% -
Caltech DB(Cars 2001)
504 93.6% 99.2%
Lara Urban Sequence 1 2716 92.6% 96.3%
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Results in adverse weather conditions
University of PatrasUniversity of Patras
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Conclusions
University of PatrasUniversity 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
15University of PatrasUniversity 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).
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