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Highlight Reel: Automated Sports Content Generation
Paul Kefer
Paul Kefer
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Highlight Reel: Automated Sports Content Generation

Develop machine learning models to classify player actions from tracking data and automatically generate game highlights.

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Thursdays
 at
5:00
P.M.
 ET /
2:00
P.M.
PT
8 weeks, 2-3 hours per week
Expert
No experience required
No experience required
Some experience required
Degree and experience required

Description

In the fast-paced world of sports streaming, automated highlight generation is becoming increasingly crucial for engaging fans and providing quick insights. In this Build Project, you'll step into the role of a Machine Learning Engineer to develop an end-to-end system for automated sports action recognition and highlight generation. You will develop a fully functional automated sports highlight generation system, including a trained action classification model, a video processing pipeline, and a highlight generation algorithm.  

Under the guidance of an experienced industry expert, you'll build machine learning models to classify player actions from tracking data, create a video processing pipeline, and implement highlight generation algorithms. You'll gain hands-on experience with cutting-edge technologies like computer vision, deep learning, and large language models, simulating the workflow of a real data science team in the sports tech industry.

Session timeline

  • Applications open
    September 5, 2024
  • Application deadline
    September 19, 2024
  • Project start date
    Week of July 8, 2024
    Week of
    October 7, 2024
  • Project end date
    Week of

What you will learn

  • Create algorithms for real-time sports action recognition.
  • Design and train machine learning models in PyTorch to identify key moments in sports footage.
  • Develop an automated system for generating sports highlight reels.
  • Analyze and optimize video processing pipelines for efficient sports content creation.
  • Apply deep learning techniques to classify and segment sports video data.

Project workshops

1
Project Overview & Data Exploration
2
Data Preprocessing & Feature Engineering
3
Model Selection & Training
4
Model Optimization & Deployment
5
Video Processing Fundamentals
6
Highlight Generation Pipeline
7
Refinement & Storytelling Enhancement
8
Final Presentations

Prerequisites

  • Intermediate Python programming skills: You should be comfortable with object-oriented programming, data structures, and working with libraries like NumPy and OpenCV
  • Basic understanding of machine learning concepts: You should be familiar with supervised learning, classification algorithms, and model evaluation metrics at the level typically covered in an introductory machine learning course.
  • Experience with data visualization: You should be able to create basic plots and charts using libraries like Matplotlib to analyze and present data effectively.
  • Familiarity with video processing concepts: While expertise isn't required, you should have a basic understanding of video frames, codecs, and simple manipulations (e.g., cutting, resizing).
  • Strong problem-solving skills: You'll need to troubleshoot issues, experiment with solutions, and apply your knowledge to real-world problems throughout the project.

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About the expert

Paul Kefer is a machine learning engineer specializing in autonomous sports broadcasting. He leverages his expertise in artificial intelligence and computer vision to develop cutting-edge systems that revolutionize how sports events are captured and broadcast. In his free time, Paul is an enthusiast of immersive technologies, exploring the realms of augmented and virtual reality. He seeks thrills through indoor skydiving and can often be found cruising along the beach on his electric longboard, enjoying the perfect blend of technology and outdoor adventure.

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