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Academic Year: 2024-2025
Assessment Introduction:
Course:
BEng Electronic Engineering
BEng Robotic Engineering
MEng Robotic Engineering
Module Code: EL3105
Stabilisation
Type of assessment: Assignment
Introduction
This Assessment Pack consists of a detailed assignment brief, guidance on what you need to
prepare, and information on how class sessions support your ability to complete successfully.
The tutor responsible for this coursework will be available on 28th of January 2025 to answer
questions related to this assessment. You’ll also find information on this page to guide you on
how, where, and when to submit. If you need additional support, please make a note of the
services detailed in this document.
Submission details; How, when, and where to submit:
Assessment Release date: 21/01/2025.
Assessment Deadline Date and time: 28/03/2024 (23:59).
Please note that this is the final time you can submit – not the time to submit!
You should aim to submit your assessment in advance of the deadline.
The Turnitin submission link on Blackboard, will be visible to you on: 03/03/2025.
Feedback will be provided by: 06/05/2025.
This assignment constitutes 50% of the total module assessment mark. You should write a
report for this assignment documenting your solutions for the tasks defined in the assignment
brief given below. The report should include a very short introduction describing the problem,
description of your adopted solutions, a more extensive description of the results and
conclusions section summarising the results. The report should be approximately 1500 words
long, plus relevant materials (References and Appendices). You should use Harvard
referencing system for this report. The report should be submitted electronically to “Video
Stabilisation” Turnitin through Blackboard.
You should submit a documented matlab/python code solving the given tasks. The code
should be self-contained, i.e., it should be able to run as it is, without a need for any
additional tools/libraries. In case, there are multiple files please create a single zip code
Disclaimer: The information provided in this assessment brief is correct at time of publication. In the unlikely event that any changes
are deemed necessary, they will be communicated clearly via e-mail and a new version of this assessment brief will be circulated.
archive containing all the files. The code should be submitted separately from the report into
Blackboard EL3105 assignment area denoted as “Video Stabilisation Code”.
Note: If you have any valid mitigating circumstances that mean you cannot meet an
assessment submission deadline and you wish to request an extension, you will need to apply
online, via MyUCLan with your evidence prior to the deadline. Further information on
Mitigating Circumstances via this link.
We wish you all success in completing your assessment. Read this guidance carefully, and
any questions, please discuss with your Module Leader.
Teaching into assessment
The tutor responsible for this coursework will be available on 28/01/2025 between 14:00 in
the 16:00 to answer questions related to this assessment.
All the algorithmic aspects necessary for the successful completion of the assignment have
been covered during the lectures, tutorial, and laboratory sessions, these include feature
detection, descriptor calculation, robust matching, estimation of a transformation aligning
matched features, tracking and image warping.
Additional Support
All links are available through the online Student Hub
2. Support with your academic skills development (academic writing, critical thinking and
referencing) is available through WISER on the Study Skills section of the Student Hub.
3. For help with Turnitin, see Blackboard and Turnitin Support on the Student Hub
4. If you have a disability, specific learning difficulty, long-term health or mental health
condition, and not yet advised us, or would like to review your support, Inclusive
Support can assist with reasonable adjustments and support. To find out more, you
can visit the Inclusive Support page of the Student Hub.
5. For mental health and wellbeing support, please complete our online referral form, or
email wellbeing@uclan.ac.uk. You can also call 01772 893020, attend a drop-in, or visit
our UCLan Wellbeing Service Student Hub pages for more information.
6. For any other support query, please contact Student Support via
studentsupport@uclan.ac.uk.
7. For consideration of Academic Integrity, please refer to detailed guidelines in our policy
document . All assessed work should be genuinely your own work, and all resources
fully cited.
8. For this assignment, you are not permitted to use any category of AI tools.
Assignment Brief
This assignment is designed to give you an insight into selected aspects of computer vision
applied to image feature extraction, feature matching, and motion compensation. You are
asked to solve various tasks including detection of image features and their robust matching,
write computer vision software as well as test your solution and interpret the results.
This assignment will enable you to:
 Deepen your understanding of the features/keypoints detection and robust matching
between features, image transformation and warping models.
 Recognise software design challenges behind implementations of computer vision
algorithms.
 Design and optimise software to meet specified requirements.
 Acquire a hands-on understanding of image-based camera motion compensation.
(These correspond to point 1, 2, 4 and 5 of the module’s learning outcomes. Module learning
outcomes are provided in the Module Descriptor)
The assignment consists of two main tasks. The first task is to explain and justify your
selected methodology for video stabilisation, specifically focusing on camera motion jitter
compensation. This should include a discussion of jitter compensation for a moving camera,
handling moving objects within the scene, depth of field, and the ability to operate in real time.
The second task is to implement the selected method using MATLAB and/or Python. You are
provided with two pre-recorded videos that increase in scene complexity. The first video
features a static scene with a jittering, but otherwise static, camera. The second video
contains a scene with moving objects. The two videos, video_seq_1.avi and
video_seq_2.avi, are available on the Computer Vision Blackboard site.
You are expected to write a MATLAB (and/or Python) program that removes the apparent
scene motion caused by the camera jitter in the video sequences. Your algorithm should be
designed to process the images sequentially, meaning that when estimating the current
image correction, it should only use the current and preceding frames. Additionally, the
algorithm should be optimized to work in real-time, with computational complexity that does
not depend on the length of the sequence.
Late work
If the report and/or code are submitted after the deadline they will be automatically flagged
as late. Except where an extension of the hand-in deadline date has been approved lateness
penalties will be applied in accordance with the University policies.
Marking scheme
Your report should contain the following elements; it will be marked in accordance with the
following marking scheme:
Item Weight
(%)
1. Justification of the adopted video stabilisation approach 30
2. Software implementation 40
3. Evaluation of the results 15
4. Presentation of the report 15
Total 100
References
Wang, Y., Huang, Q., Jiang, C., Liu, J., Shang, M. and Miao, Z. (2023) Video stabilization: A
comprehensive survey, Neurocomputing, Volume 516, pp. 205-230.
Wang, A., Zhang, L. and Huang, H. (2018) High-Quality Real-Time Video Stabilization Using
Trajectory Smoothing and Mesh-Based Warping. IEEE Access, Vol 6. pp. 25157:66.
Souza, M.R. and Pedrini, H. (2018) Digital Video Stabilization Based on Adaptive Camera
Trajectory Smoothing, EURASIP Journal on Image and Video Processing, pp. 2018:37.
Litvin, A., Konrad, J. and Karl, W.C. (2003) Probabilistic Video Stabilization Using Kalman
Filtering and Mosaicking. IS&T/SPIE Symposium on Electronic Imaging, Image and Video
Communications and Proc.
Tordoff, B. and Murray, D.W. (2002) Guided Sampling and Consensus for Motion Estimation.
European Conference on Computer Vision
Feedback Guidance:
Reflecting on Feedback: how to improve.
From the feedback you receive, you should understand:
The grade you achieved.
The best features of your work.
Areas you may not have fully understood.
Areas you are doing well but could develop your understanding.
What you can do to improve in the future - feedforward.
Use the WISER: Academic Skills Development service. WISER can review
feedback and help you understand your feedback. You can also use the
WISER Feedback Glossary
Next Steps:
List the steps have you taken to respond to previous feedback.
Summarise your achievements
Evaluate where you need to improve here (keep handy for future
work):