College application research evidence · understanding and original contribution · Seoul and online
Project courseThe student keeps the work and the record they made.
Computer Vision: Find Players and the Ball in Video
Grades 9-12: football computer vision, dataset and...
Is Computer Vision: Find Players and the Ball in Video a good fit for high school students in Grades 9-12?
Computer Vision: Find Players and the Ball in Video is a good fit for students in grades 9-12 who want to learn football computer vision through dataset and detector evaluation. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a portfolio artifact with design rationale and audience feedback. Course completion alone does not guarantee admission, an award, or a score.
Students use a working example to trace the research question behind football computer vision and how researchers use dataset and detector evaluation to test it. Running a working example is only the starting point. First understand the questions university researchers ask in this field and how they test them. Then design and test a student-owned extension: a new question, feature, model, interface, or solution. In an application or interview, the student distinguishes the source research from their own decisions, results, failed attempts, revisions, and limits.
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My child found the AI classes interesting and stayed with them. It made for a worthwhile school break. Thank you for teaching so attentively.
- Age group
- Grades 9-12
- Academic subject
- AI & Data, Arts, Media & DesignBrowse subject
- Course type
- Course
- Format
- Online or Apgujeong in person · one-to-one or small group
- Teaching language
- Korean by default, with complete English materials
- Curriculum status
- Reviewed curriculum
From open research to student-owned work
Students inspect a relevant public source, reproduce the idea, then add an original question, feature, or test. Every source is public, and each lesson names the exact page it opens.
How can I explain this course to my child?
If questions about football computer vision or dataset and detector evaluation keep making you ask why, Computer Vision: Find Players and the Ball in Video lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- football computer vision
- dataset and detector evaluation
- responsible web deployment
What does the student finish?The student leaves with a portfolio artifact with design rationale and audience feedback.
Is this a good fit?
A strong fit for students who want to understand, build, test, or responsibly use AI systems.
Course placement follows current subject and coding readiness.
Students should be ready to document sources, methods, and limits.
Advanced tools are introduced after a clear baseline.
What will my child learn?
- Explain football computer vision in clear, age-appropriate language.
- Use dataset and detector evaluation in a guided analysis or build.
- Compare evidence, test assumptions, and identify limits in responsible web deployment.
- Create a portfolio artifact with design rationale and audience feedback. Document the student's own role and decisions.
How does the course progress?
- 1Build clear foundations in football computer vision
- 2Apply dataset and detector evaluation in a guided task
- 3Compare evidence and review errors
- 4Explain a result using responsible web deployment
What counts as useful evidence?
A portfolio artifact with design rationale and audience feedback.
What should an admissions reader be able to see?
Running a working example is only the starting point. First understand the questions university researchers ask in this field and how they test them. Then design and test a student-owned extension: a new question, feature, model, interface, or solution. In an application or interview, the student distinguishes the source research from their own decisions, results, failed attempts, revisions, and limits.
Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
A university name, course title, or project source is not admissions evidence by itself. The student must explain what they understood and completed; no course guarantees admission.
The real research project this course reads
- roboflow/sports
- pinned commit
42c80c06b6b6 - licence MIT
- text files 20
The student separates the pipeline that turns football video into a top-down view, then changes one value to see what breaks. In class the project is separated into five boxes (input, memory, process, output, control). The lesson opens one of them, changes one value, and leaves the rest closed.
- InputWhat comes in?
One frame of match video and the mode to run
- Memoryopened hereWhat persists?
The pitch dimensions in coordinates, the fitted colour basis that separates teams, and a short trail of recent ball positions
- ProcessWhat transforms?
Detecting players and ball, splitting them into teams, and mapping screen coordinates onto pitch coordinates
- OutputWhat leaves, and who uses it?
A pitch diagram with player positions, and the annotated video
- ControlWhat decides when anything runs?
The runner that picks the mode and feeds frames one at a time
The files the lesson opens, by name. The course is not a walk through the repository; it opens a chosen few and says which.
One value moves and everything else stays. The right-hand column is the prediction written before the run, not a result; the work is reconciling the two.
On a narrow screen, swipe the picture sideways.
The universities and labs named here made the open projects this course reads. CIT designed the course independently; it is not an official, affiliated, or endorsed course.
Questions parents search before choosing this course
Is Computer Vision: Find Players and the Ball in Video a good fit for high school students in Grades 9-12?
Computer Vision: Find Players and the Ball in Video is a good fit for students in grades 9-12 who want to learn football computer vision through dataset and detector evaluation. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a portfolio artifact with design rationale and audience feedback. Course completion alone does not guarantee admission, an award, or a score.
Can my child take Computer Vision: Find Players and the Ball in Video online or in person, one-to-one or in a small group?
Yes. CIT offers online and in-person lessons at its Apgujeong academy in Gangnam, Seoul, with one-to-one and small-group options. A readiness consultation confirms the available format and starting point for the course.
Does my child need prior subject knowledge or coding experience for Computer Vision: Find Players and the Ball in Video?
Course placement follows current subject and coding readiness. Students should be ready to document sources, methods, and limits. Advanced tools are introduced after a clear baseline.
What will my child make or practice in Computer Vision: Find Players and the Ball in Video?
The main evidence is a portfolio artifact with design rationale and audience feedback. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can Computer Vision: Find Players and the Ball in Video show research understanding in a college application?
Running a working example is only the starting point. First understand the questions university researchers ask in this field and how they test them. Then design and test a student-owned extension: a new question, feature, model, interface, or solution. In an application or interview, the student distinguishes the source research from their own decisions, results, failed attempts, revisions, and limits. Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions. A university name or course title never guarantees admission.
How are the schedule and tuition for Computer Vision: Find Players and the Ball in Video determined?
CIT confirms the student's readiness, goal, location, class size, and current availability before recommending a course plan. The consultation and level check are free; tuition is explained before enrollment.
When should a student start?
There is no fixed intake month. CIT reviews the student's current school term, readiness, and available hours, then names the point in the course where they should begin.
I appreciated that a simple early idea was extended into an 'AI debate tool'. My child has always been interested in debate and social issues, so seeing that interest connect to the project makes me expect a more complete and distinctive result.
Choose the course after a readiness check
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