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日期：2025-03-03 09:33

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):