CSE 610 – Special Topics
Automated Analysis of Sporting Event Videos
Course Syllabus
(SUBJECT TO CHANGE)
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Instructor: |
Dr. Nalini Ratha |
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Office: |
113K Davis Hall |
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Email: |
nratha@buffalo.edu |
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Office Hours: |
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Zoom Link |
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Instructor: |
Dr. David Doermann |
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Office: |
113M Davis Hall |
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Email: |
Prefers to be contacted through Piazza |
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Office Hours: |
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Zoom Link |
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Lectures, Projects during the 14- week semester.
This course qualifies as a CSE Project Course, MS-Robotics Projects Course, and MS-AI Capstone course
Piazza Link: piazza.com/buffalo/spring2022/cse610
This course is an introduction to those areas of Artificial Intelligence that deal with fundamental issues and techniques of analysis of sports events using multimedia analysis, including computer vision and image processing. The emphasis is on physical, mathematical, and information-processing aspects of the media and sensor data analytics. Topics to be covered include video and sensor data collection, analysis for coaches, player feedback for performance enhancement and injury prevention, game highlights, and video summarization. All forms of media: text, sensors, video, and non-visual spectrum sensing
Most of the material is based on recently published research papers.
Students will be required to read technical research papers, prepare summaries, comment on the state-of-the-art, and present one or more research papers throughout the semester. Students are expected to participate in discussions and be an active part of the class.
A significant amount of work will be required in contributing to an analysis pipeline that will process video content of American football games. Various data will be provided, and students will be assigned multiple tasks according to their strengths. The project will evolve throughout the semester, but the goal is to be able to ingest an entire football game and provide an indexing and retrieval environment at the end. Students will work individually and in groups to define interfaces, requirements, and evaluation metrics.
· Sports media data: audio, video, text, sensor data
· Deep learning and machine learning methods
· Video analysis of sports content
o Segmentation
o Object detection
o Player identification
o Fan sentiment analysis
· Audio analysis of sports content
· Sensor data analysis of players
· Event detection and analysis
o player injuries
o scoring
o penalties
· Sports video summary highlights
· On-field visualization
o advertising
o field markings
o player tracking
· Audio synthesis (live commentary) for sports
· Text analysis (print media)
· Social media text analysis (players and fans)
· Post-game Player sensor data analysis (sleep, activity, …)
· At least one of the following:
o CSE 573 Computer Vision and Image Processing
o CSE 574 Introduction to Machine Learning
· AND Permission of the Instructor
o A pre-enrollment survey will be provided
Textbook: None
- Class attendance and participation is expected
- You are responsible for ALL materials presented in class and assigned to read
- Regular deliverables on the project will be graded during the course
- All assignments will be graded out of 100 points and weighted according to the table below
- All assignments will be turned in via UB Learns
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Weighting |
Assessment / Assignment |
Number |
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20% |
Class Participation |
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20% |
Homeworks |
4 |
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60% |
Projects |
2 |
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100% |
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- It is entirely your responsibility to follow the policies outlined here and by the university
- Please ask the instructor(s) if you have questions.
Late Submission Policy
- Completed project deliverables are to be submitted by their deadline (11:59pm).
- Projects: You will be allowed a total of 3 days/partial day late submissions throughout the semester. Each late day beyond the 3 allowed will reduce your grade by 50%.
- No individual project/homework will be accepted after 3 days late.
Regrading Policy to Correct Grading Errors
- Regrade requests are due no later than one (1) week after the scores are posted.
- Regrade requests must be clearly written and attached to the assignment.
- When work is submitted for regrade, the entire work may be regraded, which may result in a lower grade.
- Work done in pencil may not be considered for regrading.
Grading Policy
- No "I" (Incomplete) will be given except under provably extreme circumstances.
- There is no grade negotiation at the end of the semester.
Disabilities
- If you have a diagnosed disability (physical, learning, or psychological) that will make it difficult for you to carry out the course work as outlined or that requires accommodations such as recruiting note-takers, readers, or extended time on exams or assignments, please advise the instructor during the first two weeks of the course so that we may review possible arrangements for reasonable accommodations. In addition, if you have not yet done so, contact the Office of Disability Services.
All work must be your own
· Do not take the answers, words, ideas, or research findings of other people as yours; cite and acknowledge properly, and develop your own ideas.
· No cheating
· According to departmental policy, any violation of academic integrity will result in a Failing Grade for the course, and termination of departmental financial scholarship.
· Tools will be used to check similarity. Similar submissions will result in Failing Grade for all involved parties.
· Use of a code from an online repository (when permitted) must include proper and clearly visible attribution in your report.
· The course schedule will be provided in a separate document, as it may change in minor ways throughout the semester.