Session A14
Paper # 6137
Disclaimer — This paper partially fulfills a writing requirement for first year (freshman) engineering students at the
University of Pittsburgh Swanson School of Engineering. This paper is a student, not a professional, paper. This paper
is based on publicly available information and may not be provide complete analyses of all relevant data. If this paper is used
for any purpose other than these authors’ partial fulfillment of a writing requirement for first year (freshman)
engineering students at the University of Pittsburgh Swanson School of Engineering, the user does so at his or
her own risk.
OBSTACLE DETECTION AND TRACKING IN GROUND VEHICLES USING
STEREO VISION
Zachary Grimaldi, zpg6@pitt.edu, Mahboobin 10:00, Shixiong Jing, shj40@pitt.edu, Mahboobin, 10:00
Revised Proposal — As motor vehicle design is trending
towards the implementation of Advanced Driver Assistance
Systems (ADAS) into intelligent automobiles, computers are
beginning to take a much larger role in the driving process.
These new safety functions, such as obstacle collision
warnings, aim to build upon one another until ultimately
engineers are able to program an autonomous vehicle. Thus,
it is the role of computer engineers to ensure that new
intelligent ground vehicles are designed with a reliable and
complete perception of the environment and capability to
detect obstacles. In the context of driver assistance, the
purpose of obstacle detection and tracking systems is to
monitor the behavior of one or more obstacles in the vicinity
of a host vehicle.
Stereo vision is a method of obstacle detection that allows the
driving assistance system of an intelligent vehicle to more
accurately identify its surroundings. This technology uses two
cameras to capture an image of the same scene from two
different angles. These images are taken at a high rate,
compiled, and compared to build a 3D model of the vehicle’s
surroundings using an obstacle detection algorithm. This
algorithm analyzes a real-time view of the vehicle’s vicinity
in order to provide a stable tessellation of the environment
and estimate the path of a detected obstacle, regardless of
lighting and weather conditions. The analysis is represented
in a stereo disparity map, which is constructed by matching
corresponding points in the stereo pair in order to extract
regions of interest from the images. UV-disparity space is
computed from the disparity map, which can then be used to
calculate the location of obstacles in comparison to the
vehicle. Obstacle locations can be gathered to determine
their speed and direction with respect to the vehicle’s
intended path.
The advancement of obstacle detection and tracking using
stereo vision will allow for a faster response from the
intelligent vehicle than a human is realistically capable of.
This will replace human reaction with a computed avoidance
plan when an obstacle or another vehicle may cause a sudden
collision. A better judgment by the ADAS can therefore lead
to the avoidance of high-speed collisions. The implementation
of stereo vision obstacle detection and tracking technology
into ground vehicles is important to the automotive industry
University of Pittsburgh Swanson School of Engineering 1
2016/01/29
and to computer engineers because it can reduce the very
high number of vehicle collision-related damages, injuries,
and fatalities.
While analyzing the stereo vision obstacle detection
technology, we plan to dissect the algorithms that create the
3D modeling of the vehicle’s surroundings as well as
evaluate their efficiency and reliability. In addition, we plan
to investigate current applications of stereo vision in the
automotive industry and assess any need for future
development.
REFERENCES
[1] D. Aubert, A. Bak, S. Bouchafa. (2011). “Dynamic
objects detection through visual odometry and stereo-vision:
a study of inaccuracy and improvement sources.” (print
paper) Proceedings Machine Vision and Applications. pp.
681–697
[2] N. Bernini, M. Sabbatelli. (2014). “Real-Time Obstacle
Detection Using Stereo Vision for Autonomous Ground
Vehicles.” (print paper) Proceedings IEEE Conference on
Intelligent Transportation Systems. pp. 873-878
[3] A. Gasteratos, L. Nalpantidis. (2008). “Review of Stereo
Vision Algorithms: From Software to Hardware.”
International Journal of Optomechatronics, vol. 2, Sirakoulis,
Ed. Philadelphia: Taylor & Francis Incorporated. (print
essay). pp. 435–462
[4] I. Giosan, A. Iloie, S. Nedevschi. (2011). “UV disparity
based
obstacle
detection
and
pedestrian
classification.” Romanian Agency of Scientific Research.
(online article). PN-II-ID-PCE-2011-3-1086
[5] R. A. Hamzah. (2009). “Region of Interest in Disparity
Mapping for Navigation of Stereo Vision Autonomous
Guided Vehicle.” (print paper) Proceedings International
Association of Computer Science and Information
Technology. pp. 357-361
[6] C. Hane, M. Pollefeys, T. Sattler. (2015). “Obstacle
Detection for Self-Driving Cars Using Only Monocular
Cameras and Wheel Odometry.” (print paper) Proceedings
EU’s 7th Framework Programme. pp. 378-385
Zachary Grimaldi
Shixiong Jing
[7] A. Hanson, Z. Zhang. (1997). “Obstacle detection based
on qualitative and quantitative 3D reconstruction.” (print
paper) Proceedings IEEE Transactions On Pattern Analysis
and Machine Intelligence. pp. 15-26
[8] J. H. Kim, J. Kwon, J. Seo. (2014). “Multi-UAV-based
stereo vision system without GPS for ground obstacle
mapping to assist path planning of UGV.” (print paper)
Proceedings Institution of Engineering & Technology
Electronic Letters. pp. 1431-1432
[9] G. Klinker, B. Kormann, A. Neve, W. Stechele. (2015).
“Stereo Vision Based Vehicle Detection.” (print paper)
Proceedings Vehicle Sensors and Perception Systems. pp.
738-746
I. Giosan, A. Iloie, S. Nedevschi. (2011). “UV disparity based
obstacle detection and pedestrian classification.” Romanian
Agency of Scientific Research. (online article). PN-II-IDPCE-2011-3-1086
This paper, although written by graduate engineering
students, has been published by the Romanian Agency of
Scientific Research. Although this paper was written in 2011,
it presents a highly accurate obstacle detection system that
utilizes UV disparity based obstacle detection, filtering,
hypothesis generation, and classification as pedestrian and
non-pedestrian. When combined with the most current
algorithms, will aid us in the presentation of a complete and
functional obstacle detection system.
ANNOTATED BIBLIOGRAPHY
R. A. Hamzah. (2009). “Region of Interest in Disparity
Mapping for Navigation of Stereo Vision Autonomous
Guided Vehicle.” (print paper) Proceedings International
Association of Computer Science and Information
Technology. pp. 357-361
This article, published in a professional computer
engineering magazine, describes the process of using Matlab
software to create a stereo vision disparity map. The author
explains how the software is able to create the 3D disparity
map using two images taken from the front of a vehicle. This
article will help us explain how 3D mapping is utilized to
detect the depth of the scene as well as any obstacles.
D. Aubert, A. Bak, S. Bouchafa. (2011). “Dynamic objects
detection through visual odometry and stereo-vision: a study
of inaccuracy and improvement sources.” (print paper)
Proceedings Machine Vision and Applications. pp. 681–697
This article, published in a professional journal
specializing in machine vision, evaluates the accuracy of a
test stereo vision modeling system under different road
situations in order to study the impact of time integration and
time resolution. The paper analyzes the accuracy of the
detection system, the errors presented, and the methods used
to improve the results, which will provide us with statistical
data regarding the reliability of stereo vision.
C. Hane, M. Pollefeys, T. Sattler. (2015). “Obstacle
Detection for Self-Driving Cars Using Only Monocular
Cameras and Wheel Odometry.” (print paper) Proceedings
EU’s 7th Framework Programme. pp. 378-385
This professional conference paper, published by the
European Union’s Seventh Framework Programme, proposes
an algorithm-based approach to the extraction of static
obstacles from depth maps. The detailed benefits of using a
monocular fisheye lens to cover a wider field of view will
help us clarify current problems with stereo vision technology
and aid us in our description of possible engineering
solutions.
N. Bernini, M. Sabbatelli. (2014). “Real-Time Obstacle
Detection Using Stereo Vision for Autonomous Ground
Vehicles.” (print paper) Proceedings IEEE Conference on
Intelligent Transportation Systems. pp. 873-878
This professional conference paper, which was presented
at an IEEE Conference regarding intelligent transportation
systems, is a brief examination of the four most commonly
used 3D obstacle detection maps, including probabilistic
occupancy maps, digital elevation maps, scene flow
segmentations, and geometry-based clusters. This article will
help us dissect the algorithms utilized to create these maps
and discuss the application of additional algorithms.
A. Hanson, Z. Zhang. (1997). “Obstacle detection based on
qualitative and quantitative 3D reconstruction.” (print paper)
Proceedings IEEE Transactions On Pattern Analysis and
Machine Intelligence. pp. 15-26
This paper, published in an IEEE journal specializing in
pattern analysis and machine intelligence, describes the three
basic algorithms used to detect obstacles and compares them
against experimental data in terms of accuracy and efficiency.
This article will allow us to better explain the stereo vision
algorithms used to detect obstacles and provide us
information regarding the reliability of the algorithms.
A. Gasteratos, L. Nalpantidis. (2008). “Review of Stereo
Vision Algorithms: From Software to Hardware.”
International Journal of Optomechatronics, vol. 2, Sirakoulis,
Ed. Philadelphia: Taylor & Francis Incorporated. (print
essay). pp. 435–462
This article, from a professional journal specializing in
optomechatronics, presents an in-depth analysis of existing
stereo vision algorithms in terms of speed, accuracy,
coverage, time consumption, and disparity range. The article
offers a visual breakdown and explanation of these
algorithms that will aid us in our understanding of the
technology. However, it was published in 2008 and therefore
cannot provide the most current algorithms being tested and
implemented.
J. H. Kim, J. Kwon, J. Seo. (2014). “Multi-UAV-based stereo
vision system without GPS for ground obstacle mapping to
assist path planning of UGV.” (print paper) Proceedings
2
Zachary Grimaldi
Shixiong Jing
Institution of Engineering & Technology Electronic Letters.
pp. 1431-1432
This short professional article, published recently by the
Institution of Engineering and Technology, introduces the
very unique concept of implementing stereo vision system
into two unmanned aerial vehicles. These aerial vehicles
would provide the ground vehicle with a better sight view as
well as a movable baseline for the stereo vision system,
which would help us calculate the position of obstacles more
accurately.
G. Klinker, B. Kormann, A. Neve, W. Stechele. (2015).
“Stereo Vision Based Vehicle Detection.” (print paper)
Proceedings Vehicle Sensors and Perception Systems. pp.
738-746
This article was published by the Research and
Technology Department at BMW in a journal specializing
vehicle sensors and perception systems. The paper argues for
the integration of a flat road model in order to provide a more
current analysis of the vehicle’s surroundings. This article
will aid us in presenting an accurate evaluation of these stereo
vision algorithms because BMW has tested this detection
system in real traffic scenes.
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