START OF GAME MODEL BEATS THE OTHER GOAL MODELS (FOR NOW)
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SOLUTION ARCHITECTURE
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ABOUT APACHE BEAM
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THE SOLUTION LANDSCAPE
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FROM HLS TO JPEG
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FULLY LEVERAGE MANAGED SERVICES
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LEVERAGE THE BEAM MODEL FOR PROCESSING
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WHERE THE DATA CRUNCHING HAPPENS
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PIPELINE DEEP DIVE
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LEVERAGE THE INTERNAL LOAD BALANCER OF GKE TO GET PREDICTIONS
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DEWARPING THE BOUNDING BOXES TO GET COORDINATES
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TEAM DETECTION WITHOUT BACKGROUND SUBTRACTION
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DUMPING THE PREDICTIONS TO BIGTABLE
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LEVERAGE THE BEAM MODEL TO WINDOW THE DATA
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RESPECT THE BEAM MODEL TO GET DESIRED PARALLELIZATION
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TEST IN STREAM MODE
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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.
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