Dection of Lanes by usin HDR.... VISION SENSORS..

1. SREE KAVITHA ENGINEERING COLLEGE
UNDER THE SUPERVISION OF:
Mrs.D.SAILAJA M.Tech,(ph.D).;
Associate Professor.
Karepalli – 507122,Khammam Dt, TS
DEPT OF
ELECTRONICS AND COMMUNICATION
ENGINEERING
A LANE BOUNDARY DETECTION
METHOD BASED ON HIGH DYNAMIC
RANGE IMAGE
PRESENTED BY:
K.LAVANYA
(11C81A0457)

2. CONTENTS
• ABSTRACT
• INTRODUCTION
• HDR CAMERA
• LANE DETECTION OF HDR IMAGE
• MERGING OF IMAGES
• DIFFERENTIAL EXPOSED IMAGES
• MERG HIGH DYNAMIC RANGE IMAGE
• IMPROVED METHOD BASED ON HDR
• RESULTS
• ADVANTAGES AND DISADVANTAGES
• CONCLUSION AND FUTURE SCOPE

3. ABSTRACT
Every year many vehicle departure accidents
happen due to the driver's carelessness.
Lane Departure Warning System (LDWS) is a kind of
system which can relieve the stress of the drivers and reduce
traffic accidents.
But most traffic scences have greater dynamic range than
digital camera at present.

4.  ADAS are systems which can help the drivers in their driving
process. They are designed with a safe Human-Machine Interface and it
should increase car safety and more generally road safety.
 Nowadays the most common ADAS are navigation system, adaptive
cruise control (ACC) system, lane departure warning system (LDWS), lane
change assistance system, collision avoidance system, night vision, traffic
sign recognition and so on.
 Dynamic range is the ratio between the largest light intensity and the
smallest possible light intensity values in a scene. The dynamic range in most
scenes in the natural environment is larger than 10^5
INTRODUCTION

5. DIFFICULT SCENES FOR LDWS
Affected by adverse light conditions such as reflective road
surfaces, road with the shadows of the trees

6. HDR CAMERA

7. LANE DETECTION ON HDR
IMAGE
TAKE DIFFERENTLY EXPOSED IMAGES
Tocci’s method is the most suitable for the real time
system like the lane departure warning system.
Images taken by a camera continuous with changing
exposure time. In the first row, the images exposure time are
1/250 second, 1/1000 second, 1/60 second. In the second
row, the images exposure time are 1/60 second,1/250second,
1/15 second.
We can find out a lot of pixels are white in the properly-
exposure image., and in the under-exposure image most
pixels are almost black while in the over-exposure image
most pixels are almost white.

8. DIFFERENTLY EXPOSED IMAGES
DIFFERENTILY EXPOSED IMAGES

9. HISTOGRAM OF THE PROPERLY-EXPOSURE IMAGE HISTOGRAM OF UNDER-
EXPOSURE IMAGE
HISTOGRAM OF THE OVER-EXPOSURE
IMAGE
HISTOGRAM REPRESANTATION

10. LANE DETECTION OF HDR
IMAGE
RECOVER THE RESPONSE FUNCTION OF THE CAMERA:
We use the method Debevec proposed to calculate the
response function .Let us call the response function f. It can
be assumed that Zij is the gray scale value of the pixel which
is numbered i and exposure time is Δtj. Ei is the real
luminance of the point in the scene that pixel i take. They
meet the equation as:

11. MERG OF HIGH DYNAMIC RANGE
IMAGES
We use the differently exposed images and the
camera response function to merge a 32 bit
image and then tone mapping it to a 8 bit
image.
CAMERA RESPONSE

12. AUTO EXPOSED IMAGE AND HDR IMAGE OF THE SAME SCENE
HISTOGRAM OF THE HDR
IMAGE

13. EDGE DETECTION
 Edge detection of the image is to detect the
edge of the surrounding pixels have a gray-scale
step-like changes or changes in the roof of a
collection of pixels. In the images taken for lane
detection, the lane in the image is different from
the road. So we can detect the lane use EDGE
DETECTION ALGORITHM.
 We use Sobel operator to detect the lane
boundary. The Sobel operator use the weighted
difference of the gray scale value of the field
point of each point in the image.

14. Image binaryzation
 Before detect the line, we should do the image threshold
segmentation first. Image binaryzation is the procedure of
separating the target and the background.
 The algorithm assumes that the image to be thresholded
contains two classes of pixels or bi-modal histogram,
then calculates the optimum threshold separating those
two classes so that their combined spread.

15. Hough transform
 The final step of the detection is to detect the boundary of
the lane. Most of the lane are straight line or clothoid curve,
we can use hough transform to detect the line.
 Carried out in voting procedure.
 This voting procedure is carried out in the parameter space,
in the parameter space the object candidates are obtained as
local maxima in an accumulator space that is constructed by
the algorithm for computing the Hough transform.

16. IMPROVED METHOD BASED ON HDR IMAGE
 EXPOSURE FUSION
Exposure fusion is an alternative way to merge high dynamic range
images. Compared with the traditional HDR merging method, it
leaves out many computation procedures. By fusion the different
exposed images, the image has greater dynamic range than one
image. But without scientific calculations, the image merged by
exposure fusion is not as good as the image merged in the
traditional HDR merging method.

17. The improved method
As we all know, color image has red, green, blue channels, in the high
dynamic range merging procedure, we should calculate three times to merge a
HDR image. And the time of calculate one channel is very long. But the
computation on binary images is very quick.
So we propose a improve method.
The lane detection procedure of the method based on HDRimage is as
shown in and the lane detection procedure of the improved method
Exposure fusion in the improved method
The lane detection system is a real-time system. We should consider the
computation time first.
If we choose a complex exposure fusion method, it will not use in a real
lane departure warning system.

18. PROCEDURE OF THE METHOD BASED ON HDR IMAGE
FLOW CHART REPRESENTATION

19. PROCEDURE OF THE IMPROVED
METHOD
IMPROVED METHOD

20. Lane Departure Warning System (LDWS) is a kind of system
which can relieve the stress of the drivers and reduce traffic
accidents.
The high dynamic range image can improve the accuracy of the
lane detection method.
We proposed an improved method based on exposure fusion to
reduce the computational time of the system.
ADVANTAGES

21. DISADVANTAGES
Processing of merging HDR image is very time consuming. It
makes HDR image can't be used in real-time LDWS.
We proposed an improved method based on exposure fusion
to reduce the computational time of the system.

22. APPLICATIONS
1)LDWS
2)Lane keeping assistance
3)Blind spot recognition
4)Traffic sinal recognition
5) Pedistrain recognition

23. COMPUTATION TIME
After a series of experiments, we get the
computation time use each method. The average
computation time on a single image is about 40ms, the
average computation on HDR image is about 2 seconds, and
the computation time use the improved method is about
100ms. We can get the method based on high dynamic
range images cost too much time, it can’t be used in the
system. But the improved method based on exposure fusion
costs about 65ms, it can be used in the real-time system.
EXPERIMENTAL RESULTS

24. DETECTION RESULTS
Edge detection result of the auto exposed images
and high dynamic range image

25. CONCLUSION
We use three images with different exposure to merge a high dynamic
range image and detect the lane in the HDR image.
The experimental results show that the high dynamic range image can
improve the accuracy of the lane detection method. However, the processing of
merging HDR image is very time consuming. It makes HDR image can't be
used in real-time LDWS.
We proposed an improved method based on exposure fusion to reduce the
computational time of the system.

26. FUTURE WORK
The average computation time of with the improved method based
on exposure fusion is about 100ms. It meets the need of the lane
departure warning system.
In order to detect the lane in the image, future work on develop a
high dynamic range camera system should be excavated and utilized.
By incorporating a departure warning system, the functionalities of
the lane marking system can be enhanced.
Hough transform can be implemented on FPGA board. FPGA
implementation consumes less power also it is very compact and fast.