CSE 702 – Seminar
Automated Analysis of Sporting Event Videos
Course Syllabus
(SUBJECT TO CHANGE)
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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: |
TBD |
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Zoom Link |
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Technical Paper presentations, Literature survey.
This course does NOT qualify as a CSE Project Course, MS-Robotics Projects Course, and MS-AI Capstone course
You man NOT take this course if you took the Special Topics 610 course in the Spring of 2022.
Piazza Link: piazza.com/buffalo/summer2022/cse702
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 in-person class.
Students will be expected to produce a written survey and a recorded TED-Style talk on the topic that they survey. Optionally, they will be able to produce and report on various video analytics as part of their project.
· 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
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 |
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20% |
Class Participation |
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20% |
Paper Presentations |
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60% |
Project/Survey/TED-Talk |
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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.
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.