School of Engineering
UCLan Coursework Assessment Brief
Module Title: Computer Vision
Module Code: EL3105
Objects Detection in Video This assessment is worth
50% of the overall module
This assignment is designed to give you insight into selected aspects of computer vision applied to
object detection and tracking in video. You are asked to solve various tasks including the detection of
keypoints and their robust matching, writing computer vision software operating in a soft real-time as
well as testing your solution and interpreting the results.
This assignment will enable you to:
• Deepen your understanding of the keypoint detection and robust matching between keypoint
• Recognize software design challenges behind implementations of computer vision algorithms.
• Design and optimise software to meet specified requirements.
• Acquire a hands-on understanding of object detection in video problems.
(These correspond to point 1, 2, 4 and 5 of the module learning outcomes)
Assignment Description and Objectives
You are asked to write software to detect predefined objects in pre-recorded video sequences provided
as part of this assignment. The solution should be based on the keypoints matching technique, using
points detected respectively in the current video fame and the template images showing objects of
interest. You should explore different design options, including the selection of the keypoint detection
method, keypoint descriptor and keypoint transformation model. You should write the software using
In the first task you are asked to detect the object of interest, shown in the object_1.jpg template image,
in the video_1 sequence.
For the second task, you should modify the software from the first task, so two predefined objects shown
in object_1.jpg and object_2.jpg template images are simultaneously detected in each frame of video_2
For the third task, you should further modify the software from the task two, so it can detect multiple
instances of the same object in video_3 sequence. You should make sure that the software consistently
tracks these multiple instances across video frames.
Images object_1.jpg and object_2.jpg showing objects to be detected/tracked and the video sequences
video_1, video_2, and video_3 are all available from Blackboard.
Your report should contain the following elements; it will be marked in accordance with the following
Item Weight (%)
1. Detection of a single object in video_1 35
2. Detection of two different objects in video_2 30
3. Detection of two instances of the same object in
4. Presentation of the report 15
R. Hartley, A. Zisserman, "Multiple View Geometry in Computer Vision," Cambridge University Press,
H. Bay, T. Tuytelaars, L.V. Gool, “SURF: Speed Up Robust Features”, European Conference on
Computer Vision , ECCV’2006, pp. 404-417. 2006.
H. Al-Sahaf, et al., “Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming
Approach for Evolving Rotation-Invariant Texture Image Descriptors”, IEEE Transactions on
Evolutionary Computations, Vol. 21, No. 6, pp. 825-844, 2017.
Matlab help on: “Object Detection in Cluttered Scene Using Point Feature Matching”
PREPARATION FOR THE ASSESSMENT
The assignment is to be introduced and discussed during laboratory sessions on Friday 26th of February
as well as 5th and 12th of March. During those session the background of this assignment will be
introduced; the data structure will be explained, and the expected results will be elucidated with
examples. The set of software tools available for the assignment will be also described.
All the algorithmic aspects necessary for the successful completion of the assignment were or will be
covered during the lectures, tutorial, and laboratory sessions, these include keypoint detection, keypoint
descriptor calculation, robust matching of the keypoints, and estimation of a transformation aligning
RELEASE DATES AND HAND IN DEADLINE
Assessment Release date: 5/03/2021 Assessment Deadline Date and time: 14/04/2021 - 23:59
Please note that this is the final time you can submit – not the time to submit!
Feedback for this assessment will be provided by 05/05/2021.
Submission of assignment work
This assignment constitutes 50% of the total module assessment mark. You should write a report for
this assignment documenting your solutions for the three tasks defined above. The report should include
a very brief 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 Turnitin
You should submit a documented matlab code solving the defined above three tasks. The code should
be self-contained, i.e. it should be able to run as it is, without a need for any addition tools/libraries. You
might be asked to explain operation of your software. The code should be submitted separately from
the report into Blackboard EL3105 assignment area denoted as “Assignment Code and Videos”.
You are also expected to submit three videos (in avi format), one for each of the three tasks,
demonstrating performance of your solution on the three videos video_1, video_2, and video_3 provided
as part of this assignment. The submitted videos are the integral part of the assignment, up to 50 marks
could be deduced in case these videos are missing from your submission. The videos should be
submitted together with the code into Blackboard EL3105 assignment area denoted as “Assignment
Code and Videos”.
Work submitted electronically may be submitted after the deadline to the same Turnitin assignment slot
and will be automatically flagged as late.
Penalties for late submission
Except where an extension of the hand-in deadline date has been approved lateness penalties will be
applied in accordance with University policy as follows:
(Working) Days Late Penalty
1 - 5 maximum mark that can be achieved: 40%
more than 5 0% given
During the induction and via your student handbook, you were informed of the serious consequences
of using or attempting to use unfair means to enhance performance. This includes plagiarism. The work
submitted must be your own and any information and material used properly identified and
The University operates an electronic plagiarism detection service where your work may be uploaded,
stored and cross-referenced against other material. The software searches the World Wide Web and
extensive databases of reference material to identify duplication.
For detailed information on the procedures relating to plagiarism, please see the current version of the
University Academic Regulations.
HELP AND SUPPORT
• The support for this assignment will be provided during scheduled extra session.
• For support with using library resources, please contact subject Mr. Robert Frost or . You will find
links to lots of useful resources in the My Library tab on Blackboard.
• If you have not yet made the university aware of any disability, specific learning difficulty, long-term
health or mental health condition, please complete a Disclosure Form. The Inclusive Support team
will then contact to discuss reasonable adjustments and support relating to any disability. For more
information, visit the Inclusive Support site.
• To access mental health and wellbeing support, please complete our online referral form.
Alternatively, you can email email@example.com, call 01772 893020 or visit our UCLan Wellbeing
Service pages for more information.
• If you have any other query or require further support you can contact The , The Student
Information and Support Centre. Speak with us for advice on accessing all the University services
as well as the Library services. Whatever your query, our expert staff will be able to help and support
you. For more information , how to contact us and our opening hours visit Student Information and
• 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 prior to
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