Video Analytics for Football Games
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore video analytics for football games in this Devoxx conference talk by Sven Degroote. Dive into a use case utilizing Apache Beam to analyze and process near-real-time football game stream feeds. Learn how to determine events such as game start, team detection, player tracking, and ball tracking, while performing analytics on video duration, ball possession, and score. Discover the implementation of Apache Beam Dataflow runner with Python SDK to create streaming pipelines, using sliding windows to chunk video frames for machine learning model input. Understand the deployment of ML models on GPUs via TF-serving on Kubernetes, and the visualization of features using Google Cloud Bigtable. Gain insights into the project's architecture, including Google Cloud Pub/Sub, Google Kubernetes Engine, and the challenges faced in player detection, background subtraction, and coordinate transformation. Learn about leveraging managed services, pipeline deep dives, and testing in stream mode to create an efficient video analytics system for football games. Read more