This document describes a rear vehicle detection system using computer vision techniques for collision avoidance. The system aims to detect vehicles from a mounted camera on a host vehicle and alert the driver of an impending rear-end collision. It utilizes the fast radial symmetry transform to detect the blooming effect of vehicle rear lights under various illumination conditions. Candidate light pairs are identified and their symmetry and distance from the camera are estimated. The system achieves high detection rates on benchmark datasets, including in adverse weather. Future work involves correlating vehicle and braking detection with estimating driver attention level.

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. 2
Introduction
University of Patras
Motivation
• Accident prediction and prevention is the ultimate solution
for maximizing driving safety
Goal
• Develop a vehicle-mounted driver assistance system
aiming at alerting the driver about an impeding collision
Implementation
• Reliable vehicle detection even under adverse conditions
based on on-board mounted camera using computer
vision techniques

3. 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. 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

5. 5
Previous work
University of Patras
Visual Features
• Symmetry
• Shadow
• Edges, corners
• Texture
• Vehicle Lights
Motion
• Optical flow
• Differential
techniques
Appearance
• Local or Global
feature extraction
• Pattern Classification
Z. Sun, G. Bebis, R.Miller, “On-road Vehicle Detection: A Review”

6. 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. 7
Proposed System Overview
University of Patras

8. 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. 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 Image a* plane of L*a*b* Fast Radial Transform

10. 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. 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. 12
Experimental results
University of Patras
Database
NUMBER 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%

13. 13
Results in adverse weather conditions
University of Patras

14. 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
Future work
 Correlate the danger of an impeding collision (vehicle detection
and braking recognition) with the level of attention of the driver
(gaze estimation).

15. 15
University of Patras

16. 16
Thank you for your attention!
evskodras@upatras.gr