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L3 Assignment – SSA – Computer Vision
Autonomous Vehicles: Using Stereo Vision for Object Distance Ranging
Background
Autonomous road vehicles and advanced driver assistance systems are fast becoming a reality.
Computer Vision is increasingly being used to allow such vehicles to understand the road
environment around them based on imagery from on-board forward facing cameras.
In this assignment we are dealing with the automatic detection of objects, and the estimation of their
distance from the vehicle (i.e. ranging), within stereo video imagery from an on-board forward
facing stereo camera. This can be performed by integrating the use of depth (disparity) information
recovered from an existing stereo vision algorithm with an object detection algorithms. Knowledge
of the distance of objects that have the potential to move within the scene (i.e. dynamic objects,
such as pedestrians/vehicles) assists both automatic forward motion planning and collision
avoidance within the overall autonomous control system of the vehicle.
The low cost and high granularity (i.e. full-scene) 3D information available from stereo vision
means that classification (i.e. type) and distance of objects in front of the vehicle the vehicle can be
detected more readily than with radar or LiDAR (laser) sensing technologies.
As such, we have a set of still image pairs (left and right) extracted from on-board forward facing
stereo video footage under varying illumination conditions and driving conditions. Your task is to
design and prototype a computer vision system to estimate the range (distance) of specific objects
of interest from the vehicle at any given point in the journey. You will develop this prototype
system using Python with the OpenCV library and the techniques covered in the module.
This is a real-world task, comprising a real-world image set. As such, this is an open-ended
challenge type task to which a perfect solution that works perfectly over all the images in the
provided data set may not be possible.
Task Specification – Object Detection and Distance Ranging
You are required to develop a system that correctly detects pedestrians and vehicles within the
scene in-front of the vehicle and estimates the range (distance in metres) to those objects. For your
solution may make use of the provided state-of-the-art object detection approach (e.g. You Only
Look Once - YOLO, yolo.py) or alternatively you may research and use your own.
For each detected object you are required to make a single estimate of its distance from the vehicle
using some form of stereo vision. You can do this using either dense stereo (as provided), sparse
(feature point based) stereo vision or perhaps some other variant.
Additionally, some example images in the provided test sequences may suffer from significant
image noise making disparity calculation challenging using either technique. The road scene itself
will change in terrain type, illumination conditions, clutter and road markings – ideally your
solution should be able to cope with all of these. All examples will contain a clear front facing view
2019/2020 – Department of Computer Science, Durham University (TPB, v0.3) 1
L3 Assignment – SSA – Computer Vision
of the road in front of the vehicle only – your system should report all appropriate objects instances
it can detect recognising this may not be possible for all cases within the data set provided.
Initially you are only required to identify two types (class) of dynamic objects – pedestrians and
vehicles – but you may choose to extend this as time allows and with consideration of the available
credit in the marking scheme provided (which is limited for this aspect).
As this is only a prototype – efficiency of your approach is less important than performance.
Additional Program Specifications
Additionally, to facilitate easy testing, your prototype program must meet the following functional
requirements:
● Your program must contain an obvious variable setting in the top of the main code file that
allows a directory containing images to be specified. e.g.
master_path_to_dataset = "TTBB-durham-02-10-17-sub10"
from which it will cycle through each stereo pair in turn processing it for object detection
and distance ranging prior to displaying it. A basic example (stereo_disparity.py) for cycling
through the data set of images and computing the stereo disparity is provided.
● When objects are detected within a scene your solution must display a coloured polygon on
the left (colour) image highlighting where the object is and also a distance estimate to the
object obtained from the corresponding stereo depth information of the scene (see example
in Figure 2, for which you can ).
● Furthermore, for each image file it encounters in the directory listing it must display the
following to standard output:
filename_L.png
filename_R.png : nearest detected scene object (X.Xm)
where “filename” is the current image filename and X.X is the distance in metres to the
current nearest dynamic scene object detected within the scene. When no objects can be
detected, output a zero distance for dynamic objects. Your final program must run through
all the files as a “batch” without requiring a user key press or similar.
● Your program must operate with OpenCV 4.1.x on the lab PCs.
2019/2020 – Department of Computer Science, Durham University (TPB, v0.3) 2
Figure 1: Example left (colour), right (greyscale, rectified) and corresponding disparity calculated
using the example python code provided for the assignment.
L3 Assignment – SSA – Computer Vision
Sample Data & Example Software
The sample data provided is a set of 1449 sequential still image stereo pairs extracted from onboard
stereo camera video footage (see example – Figure 1). These images have been rectified
based on the camera calibration and you do not need to perform stereo calibration yourself.
The full set of images is available as a single ZIP file from DUO as follows:
TTBB-durham-02-10-17-sub10.zip
Be aware that this data set is still large! (~2Gb, this is the nature of this business).
Two sets of example python scripts are also provided as a starting point as follows:
• stereo_disparity.py – cycles through the stereo dataset (TTBB-durham-02-10-17-sub10) and
calculates the dense disparity from the left and right stereo images provided (lecture 5)
• stereo_to_3d.py – projects a single example stereo pair to a 3D in order to show how to
obtain 3D distance information for a given pixel location in the scene, write a point cloud of
this data to file and how to example back-projection from 3D to the 2D image (lecture 5)
Available from - https://github.com/tobybreckon/stereo-disparity
• yolo.py – an example object detection approach which you can use “out of the box” for your
object detector for the purposes of this assignment (at the moment this can be treated as a
black box detection component, although the full details will be taught in lectures 9/10).
• surf_detection.py – an example feature point matching code that can be used to match
SURF, SIFT or ORB feature points from one region of an image to another (e.g. in order to
facilitate sparse stereo vision between matched points)
Available from - https://github.com/tobybreckon/python-examples-cv
2019/2020 – Department of Computer Science, Durham University (TPB, v0.3) 3
Figure 2: Illustrative polygon outline of the detected scene objects, with distance displayed
and abbreviated class label inset, drawn on the left (colour) image – your display can differ.
L3 Assignment – SSA – Computer Vision
Marks
The marks for this assignment will be awarded as follows:
● Overall design and implementation of your solution including aspects of:
○ any image pre-filtering or optimization performed (or similar first stage processing)
to improve either/both object detection or stereo depth estimation
○ effective integration of (existing) object detection and dense stereo ranging
○ object range estimation strategy for challenging conditions 30%
● General performance on object ranging from stereo vision**
(taking into account accuracy under challenging conditions) 20%
● Clear, well documented and presented program source code 5%
● Report:
○ Discussion / detail of solution design and choices made 10%
○ Qualitative and/or quantitative evidence of performance 10%
● Additional credit will be given for one or more of the following:
○ the design and use of an alternative sparse stereo based ranging approach
○ the design and use of another variant approach to stereo based ranging
○ the use of heuristics or advanced processing/optimisation to improve performance
○ Qualitative and/or quantitative comparison of multiple such ranging approaches
(for any of the above up to a maximum, dependent on quality) 25%

Total: 100%
[ ** as supporting evidence for this part you are required to submit a video file of your system in
operation over a sample of the data – make sure your video shows both the colour and disparity
images. This can be constructed using OpenCV directly or any tool of your choice. File size must
be less than 20Mb in size, video format in use must playback in the VLC tool –
https://www.videolan.org/]
Submission :
You must submit the following:
● Full program source code together with any required additional files for your final
solution to the above task as a working python script meeting the above “additional
program specifications” for testing. Include all supporting python files and clear
instructions (e.g. in a README.txt) on how to run it on the stereo dataset (TTBB... images).
● Example video file showing general performance on some of the example data (see above)
● Report (max. 750 words) detailing your approach to the problem and the success of your
solution in the task specified. Provide any illustrative images (as many as you feel
necessary) of the intermediate results of the system you produce (overlays, results of
processing stages etc.) Remember that any titles, captions, tables, references, and graphs do
not count towards the total word count of the report.
Summarise the success of your system in detecting and range estimating scene objects in the
data set. Submit this as a PDF (not in any other format).
2019/2020 – Department of Computer Science, Durham University (TPB, v0.3) 4
L3 Assignment – SSA – Computer Vision
Make it clear in the initial comments of your source code how to run your Python script. Your
executable must run on one of the lab based PCs (either on Windows or Linux via OpenCV
4.1.x) – ensure compatibility before submission.
Plagiarism: You must not plagiarise your work. You may use program source code from the
provided course examples, the OpenCV library itself or any other source BUT this usage must be
acknowledged in the comments of your submitted file. Automated software tools (e.g.
https://theory.stanford.edu/~aiken/moss/) may be used to initially detect cases of potential source
code plagiarism in this practical exercise which will include automatic comparison against code
from previous year groups. Attempts to hide plagiarism by simply changing comments/variable
names will be detected.
You should have been made aware of the Durham University policy on plagiarism. Anyone unclear
on this must consult the course lecturer prior to submission of this practical.
To submit your work create a directory named as your username (e.g. cxfh123). Place all required
files in this directory using, ZIP compress/archive this entire directory structure (not rar or .z7 or
anything else please - as this breaks the automated extract/test tools) and submit it via DUO (late
submissions will be penalised following departmental policy).
Submission Deadline:– 2pm (UK time) on 6th December 2019
2019/2020 – Department of Computer Science, Durham University (TPB, v0.3) 5

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