Monocular Vision for Collision Avoidance in Vehicles
by
Kyle John Veldman
Submitted to the
Department of Mechanical Engineering
in Partial Fulfillment of the Requirements for the Degree of
Bachelor of Science in Mechanical Engineering
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Department of Mechanical Engineering
May 22, 2015
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Kamal Youcef-Toumi
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Certified by:
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1
2
Monocular Vision for Collision Avoidance in Vehicles
by
Kyle John Veldman
Submitted to the Department of Mechanical Engineering
on May 22, 2015 in Partial Fulfillment of the
Requirements for the Degree of
Bachelor of Science in Mechanical Engineering
ABSTRACT
An experimental study facilitated by Ford Global Technologies, Inc. on the potential substitution
of stereovision systems in car automation with monocular vision systems. The monocular
system pairs a camera and passive lens with an active lens. Most active lenses require linear
actuating systems to adjust the optical parameters of the system but this experiment employed an
Optotune focus tunable lens adjusted by a Lorentz actuator for a much more reliable system.
Tests were conducted in a lab environment to capture images of environmental objects at
different distances from the system, pass those images through an image processing algorithm
operating a high-pass filter to separate in-focus aspects of the image from out-of focus ones.
Although the system is in the early phases of testing, monocular vision shows the ability to
replace stereovision system. However, additional testing must be done to acclimate the
apparatus to environmental factors, minimize the processing speed, and redesign the system for
portability.
Thesis Supervisor: Kamal Youcef-Toumi
Tile: Professor of Mechanical Engineering
3
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to my thesis advisor, Professor Youcef-Toumi, and
the amazing group of people that he has assembled in the Mechatronics Research Lab.
I owe a great deal to Iman Soltani Bozchalooi, a doctoral candidate in the Mechatronics Research
Lab. I've learned a great deal from him throughout this process and will be forever grateful for
his tutelage.
I would also like to thank the other members of the Mechatronics Research Lab for making me
feel so welcome, exposing me to their heavily diversified endeavors, and for helping me with my
research. It has truly been a pleasure to work with all of you during my time here.
I gratefully acknowledge funding support from Ford Global Technologies, LLC with Mohsen
Lakehal-ayat as program manager
Finally, I would like to thank my family and friends for all their support and encouragement
along the way.
4
TABLE OF CONTENTS
A cknow ledgem ents ........................................................................................................
List Of Figures..................................................
4
ERROR! BOOKMARK NOT DEFINED.
Introduction.......................................................................................................................
7
Background .......................................................................................................................
9
Requisite Optics .....................................................................................................
9
Focus Tunable Lenses...............................................................................................
9
Experim ent ......................................................................................................................
11
System D esign ..........................................................................................................
11
H ardw are.....................................................................................................................
11
Softw are ......................................................................................................................
12
Im age Processing Algorithm ....................................................................................
13
Im plem entation ........................................................................................................
15
D iscussion.........................................................................................................................
18
Conclusion and Recommendations ................ ERROR! BOOKMARK NOT DEFINED.
Bibliography ....................................................................................................................
21
Appendix A: Xenon Ruby 2.5/25 Data Sheet.............................................................
22
Appendix B: Optotune 10-30-Ci Specification Sheet................................................
24
Appendix C : LabV IEW Block D iagram s .....................................................................
31
5
LIST OF FIGURES
Figure 1: Application of the Optotune tunable lens technology to change the
geometry of two types of lenses. In the upper images, the lens is transformed from
convex to concave by pushing the ring that forms the lens towards the container,
filling the lens with liquid. In the lower images, a ring pushes on the outer
membrane moving the liquid to the center of the lens ..............................
10
Figure 2: A high-level breakdown of the experimental system into its hardware
and software components. (Icons designed by Freepik)..........................11
Figure 3: The first iteration of the monocular vision setup showing a) the Optotune
EL-10-30-Ci tunable focus lens, b) the Ruby 2.5/25 lens, and c) the IDS UL3220C P cam era...........................................................................................
12
Figure 4: The Relevant Area selection (shown in red) for the closest plane of focus
(left) and the farthest plane of focus (right) after horizon removal .......
14
Figure 5: Image post segmentation and post horizon removal. Note: the Relevant
Area for this image is the entire field of view...........................................
15
Table 1: A breakdown of the image processing algorithm in LabVIEW .... 17
6
INTRODUCTION
As vehicle autonomy has become an increasingly popular topic in engineering fields, the
US Department of Transportation has established a number of policies used to govern the
emerging technology and to better provide guidance to states or institutions that allow testing of
these technologies. One such policy [1], released by the National Highway Traffic Safety
Administration, defined the levels of vehicle automation as follows:
(1) No-automation - The driver is in complete control of all vehicle functionality at all
times.
(2) Function-specific Automation - Vehicle employs the use of one or more specific
functions (i.e. electronic stability control).
(3) Combined Function Automation - At least two primary control functions are
automated to relieve the driver from having to monitor these functions (i.e. adaptive
cruise control).
(4) Limited Self-Driving Automation - The driver can seize control of safety-critical
functions in the event of dangerous conditions (i.e. hazardous weather) but the vehicle
can operate independently absent of such events.
(5) Full Self-Driving Automation - The vehicle monitors conditions and controls
functionality with minimal input (i.e. destination selection) on the part of the driver
during the entire trip.
The lower levels of vehicle autonomy are being continually supplemented as current research
supplies technical innovation; the focus being Combined Function Automation.
Adaptive cruise control, lane detection, and blind spot monitoring are three examples of
-
the dozens of automated functionalities that vehicles can now employ. Utilizing stereo vision
7
deriving information about a scene from multiple cameras - has allowed a number of these
technologies and led to various advancements including collision avoidance systems. These
systems alert the driver when a collision is imminent and can sometimes seize autonomous
control to stop the vehicle. However, because stereo vision relies on multiple cameras there is an
accompanying spatial requirement for the camera systems which can limit potential applications.
This thesis explores the applicability of monocular computer vision - solely relying on a
single camera for image processing - as opposed to the more popular stereo vision systems. If
employed, monocular vision could lead to more diverse applications due to its condensed size
and versatility.
8
BACKGROUND
Requisite Optics
A compound lens system is a series of one or more lenses. In the case of this
experimental setup, an active lens and passive lens are coupled together. The passive lens has a
fixed focal length - the measure of how strongly the system converges or diverges light - while
the active lens has an adjustable focal length. By adjusting this focal length of the active lens,
the plane of focus of the system - the plane perpendicular to the optic axis of the system where
the focal point lies - can be changed which allows the system to focus on objects at different
distances.
The more common stereovision systems use two or more cameras that allow "3D
information [to] be obtained from a pair of images, also known as a stereo pair, by estimating the
relative depth of points in the scene" [3]. These two images compare the same scene from two
angles and use triangulation to retrieve the desired information.
An associated difficulty with compound lens systems is that the depth of focus - the
distance in front of and behind the plane of focus where objects appear in focus - becomes larger
as you move farther from the system. Therefore, when looking at objects a significant distance
away from the compound lens system, more and more objects appear in focus despite their
varying distance from the setup.
Focus Tunable Lenses
Most compound lens systems - apparatuses that contain more than one lens - use lenses
of solid glass or plastic. Systems with adjustable magnification or zoom (i.e. cameras) are
typically comprised of these setups and employ a series of passive (stationary) lenses and active
(dynamic) lenses; in such systems, active lenses are moved backward or forward to adjust the
9
zoom or focus. However, Optotune has developed a different form of active lens that employs
similar mechanics to those of the human eye.
The eye contains elastic lens materials which change shape to adjust focus [4]. Optotune
adopted this mechanism to create "focus tunable lenses [which] are shape-changing lenses based
on a combination of optical fluids and a polymer membrane" [2]. The geometry of the lens (see
Figure 1) can be altered by controlling "A circular ring that pushes onto the center of the
membrane [that] shapes the tunable lens" [2]. These changes are achieved by controlling the
amount of current that is passed to the Optotune system which alters the focal length.
(a)
(b)
(d)
(C)
Figure 1: Application of the Optotune tunable lens technology to change the geometry of two
types of lenses. In images (a) and (b) the lens is transformedfrom convex to concave by pushing
the ring thatforms the lens towards the container,filling the lens with liquid. In images (c) and
(b) a ringpushes on the outer membrane moving the liquid to the center of the lens [2].
Using the Optotune lens has a number of advantages. Being able to alter the geometry
from one extreme (concave) to another (convex) is analogous to moving a lens of fixed geometry
several centimeters within a compound system, but this removes the need for linear actuation.
This results in a corresponding drop in required power and makes the entire system more
10
compact. Due to the material properties of the focus tunable lens, the apparatus will be more
robust and lighter weight. To summarize, there are "five main advantages of focus tunable
lenses over traditional optics: compact design, less mechanics, fast response, low power, and less
tolerance sensitivity" [2].
11
EXPERIMENT
System Design
The monocular vision system can be divided into two primary components, hardware and
software, with both being controlled by a central computer (see Figure 2).
(4
Figure 2: A high-level breakdown of the experimental system into its hardware and software
components.
The central controller (1) passes an oscillating current to (2) the active lens of the compound lens
system, changing the plane of focus. As the plane of focus shifts, (3) the camera captures images
to be sent to the controller. The controller then (4) segments the image, (5) applies a high pass
filter to those segments to determine whether the object lies in the focal plane of the image, and
(6) examines the past analyses on that segment to determine where the object lies in the series of
images [5][6].
Hardware
The compound lens system has three parts for this first iteration (see Figure 3). The first
is the camera for which an IDS UI-3220CP was selected for its global shutter used to capture fast
moving objects; similarly, the camera's complementary metal oxide semiconductor (CMOS)
sensor allows for high quality images in high dynamic, high contrast scenes. These and other
features make the camera highly suitable for machine vision applications. A Ruby 2.5/25 Lens
12
was selected for the passive lens due to its robustness and ability to maintain set optical
parameters during continuous use (see Appendix A for more technical information). An
Optotune focus tunable lens, specifically the EL-10-30-Ci industrial version (specifications
available in Appendix B), was chosen to avoid the obstacles that accompany an active lens that
requires mechanical actuation [5][6].
(c)
Figure 3: The first iteration of the monocularvision setup showing a) the Optotune EL-10-30-Ci
tunable focus lens, b) the Ruby 2.5/25 lens, and c) the IDS UI-3220CPcamera.
Software
LabVIEW by National Instruments (NI) is being utilized at this stage to moderate the
system. The Optotune active lens Block Diagram (LabVIEW files provided by Optotune) can be
found in Appendix C. The NI Vision Acquisition Software and Advanced Signal Processing
Toolkit were used to accommodate the image processing required; the Vision Acquisition
Software creates an interface with the compound lens system and the Advanced Signal
Processing Toolkit employs a high pass filter on the captured images to perform edge detection.
13
LabVIEW is essential to the experimental setup. Each image must be labeled with the
corresponding current that was supplied to the active lens when the image was taken and the
matching distance to the plane of focus of the camera. Because all of this information must be
integrated simultaneously, LabVIEW provided the optimal functionality at this phase.
Image ProcessingAlgorithm
As the plane of focus of the camera system oscillates from its near to far extreme
repeatedly by controlling the current passed to the camera, each frame is saved along with the
associated current value. Those images are then sent through the stages of the systems image
processing.
The first phase is to parse the image into the Relevant Area. When the plane of focus is
nearest the camera, the Relevant Area comprises the entire image; everything in the field of view
is important as they could be potential hazards. When the plane of focus is farthest from the
camera, much less of the image is significant because the objects of importance will be much
smaller. Therefore, the Relevant Area will be much smaller. An example of this application is
shown in Figure 4.
Figure 4: The Relevant Area selection (shown in red) for the closestplane offocus (left)
and the farthestplane offocus (right).
The Relevant Area of the image is then segmented into a grid (see Figure 5). The boxsize within this grid is varied with the distance to the plane of focus. As the plane of focus
14
moves farther from the driver, the potential threats (i.e. other cars) will occupy smaller segments
of the image so to be able to differentiate relevant objects, the box-size will have to adjust
accordingly.
Figure 5: Image post segmentation. Note: the Relevant Area for this image is the entire field of
view.
Once the image is segmented, the entire image is passed through a wavelet
transformation. The detail coefficients are kept for further processing and the wavelet transform
can be interpreted as a high-filtering step. This effectively removes the areas that are out of
focus and leaves the edges of whichever objects lie in the plane of focus. The remainder is then
used to compute the energy within each box of the segmented image to be used for further
analysis. The more objects within the plane of focus, the higher the energy.
This same process is undergone by every image. The archive of information is then
analyzed for energy spikes that could correspond to potential collisions or obstacles [5][6].
Implementation
The image processing algorithm was implemented in LabVIEW and showed promise in
this phase of the project. Table 1 shows a breakdown of the relevant portions of the LabVIEW
block diagrams. Appendix C contains the entire block diagram for additional reference.
15
Image
Processing
Step
Description
LabVIEW Screenshot
Image data is sent
from the camera to
rGre
6)
the computer.
Wavelet
resulting array is
Analysis
used for further
analysis.
Segmentation
The array resulting
from the high-pass
filter is separated
and Energy
into the desired
Summation
block size and the
energy of each
block is derived.
-
High Pass
Filter and
The image is passed
through a high pass
filter and the
.
Capture
Or'
int32 arraySize, horizDivider, vertDivider;
int32 i, j, n;
lint32 currentRow, currentCol;
int32 firstRun;
float64 currentVal;
horizDivider= 30;
vertDivider = 40;
arraySize = sizeOfDim(sourceO);
float64 a[8)[8);
for(i=0;i<arraySize;i++){
currentRow =floor(i/horizDivider);
for0j=0;j<arraySize;j++){
cuffrentCol= flooroj/vertl)ivi der);
currentVal = pow(source[i]U],2);
alcurrentRow)[currentCol]= aIcurrentRow][currentCol]+ currentV
16
10
Historical
Analysis
Each segmented
block of the image
is compared over
multiple frames.
fleat64
x int32
Y
V
depth1501
dept
Y deptbtxl= hist[xjjyj(
ct
Sin:71 Update
Table 1: A breakdown of the image processingalgorithm in LabVIEW.
17
(Strip Chart)
DISCUSSION
This experiment successfully used LabVIEW to apply an image processing algorithm to a
monocular vision system while simultaneously controlling said system. The system - comprised
of a passive lens, an Optotune adjustable lens, and a camera - was able to capture images in the
lab environment of objects at varying distances from the camera. Each frame was then
successfully passed through a wavelet analysis and segmented. Analyzing images taken over
multiple cycles was not accomplished within the scope of this project.
18
CONCLUSION AND RECOMMENDATIONS
Based on preliminary testing, the monocular vision system shows the potential to
substitute for the more commonly utilized stereovision system for collision avoidance and
detection. Although stereovision has the advantage of accounting for depth by using multiple
cameras, a single-camera system - using an oscillating active lens and image processing
algorithm - seems to provide a suitable means of machine vision. The success found in a lab
environment at this stage suggests that vehicular applications are feasible.
However, seeing as this project is still in an early phase, a number of steps must be
completed before monocular vision's viability for use in traffic can be confirmed.
(1) All testing was done in the lab environment under ideal conditions, for the next phase
testing needs to be done outdoors to account for environmental factors.
(2) Objects used for testing were used for their convenient geometry. The system needs to be
tested using vehicles for more accurate results (indoors - in a parking garage - and
outdoors).
(3) The final monocular vision setup needs to be portable. The current system is heavily
reliant on a desktop's processing power. Future iterations need to be moveable, robust,
and compact.
(4) The final product needs to be able to complete five cycles - moving from farthest plane
of focus to the closest - per second, so the cycle time needs to be optimized.
(5) To speed up processing speed, a Relevant Pixel Ratio (RPI) - the ratio of utilized pixels
to total pixels - needs to be introduced. For objects closest to the vision system, every
pixel is not required for data processing because the significant objects will appear rather
large; therefore, the RPI will be low. Conversely, the RPI will be highest when the plane
19
of focus is farthest from the camera seeing as the relevant objects will be smaller in the
image.
20
BIBLIOGRAPHY
[1] United States. U.S. Department of Transportation's National Highway Traffic Safety
Administration. Automated Vehicles Policy. Washington: GPO, 2013. Print.
[2] "Focus tunable lenses." optotune. N.p., 2013. Web. 17 May 2015.
<http://www.optotune.com/ >.
[3] "Stereo vision for depth estimation." Math Works. N.p., 2015. Web. 21 May 2015.
<http://www.mathworks.com/discovery/stereo-vision.html>.
[4] "Accomodation." Hyperphysics. Georgia State University, n.d. Web. 21 May 2015.
<http://hyperphysics.phy-astr.gsu.edu/hbase/vision/accom.html>.
[5] "Depth Mapping Using Active Lenses." MIT-Ford Internal presentation. Massachusetts
Institute of Technology. Cambridge, MA. Feb 2015. Print.
[6] "Depth Mapping Using Active Lenses." MIT-Ford Internal presentation. Massachusetts
Institute of Technology. Cambridge, MA. Nov 2014. Print.
"Xenon-RUBY Lens." Schneider Kreuznach. N.p.: n.p., 2013. 1-2. Web.
<http://www.schneiderkreuznach.com/en/industrial-solutions/lenses-andaccessories/products/118-oe9mm-lenses/>.
"Fast Electrically Tunable Lens EL-10-30 Series." optotune. N.p.: n.p., 2013. 1-15. Web.
<http://www.optotune.com/images/products/Optotune%20EL-10-30.pdf>.
21
APPENDIX A: Xenon Ruby 2.5/25 Data Sheet
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Image Processing
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