Completed
Intro
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Classroom Contents
Video Analytics for Football Games
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- 1 Intro
- 2 PLAYER AND BALL CAN BE DETECTED PER FRAME
- 3 EVENT DETECTION REQUIRES SEQUENCE OF FRAMES
- 4 PROJECT CONTEXT
- 5 THE PROBLEM LANDSCAPE
- 6 TWO SOLUTION PARTS
- 7 THE DATA FACTS
- 8 LEVERAGE THE MODEL TO SPEED UP THE LABELING
- 9 THE MODELS
- 10 PLAYER DETECTION: FIELD TRANSFORM
- 11 A WALK IN FEATURE SPACE
- 12 SUBTRACT BACKGROUND TO REMOVE THE NOISE
- 13 COORDINATES AS UNLOCKED DOWNSTREAM FEATURE
- 14 START OF GAME MODEL BEATS THE OTHER GOAL MODELS (FOR NOW)
- 15 SOLUTION ARCHITECTURE
- 16 ABOUT APACHE BEAM
- 17 THE SOLUTION LANDSCAPE
- 18 FROM HLS TO JPEG
- 19 FULLY LEVERAGE MANAGED SERVICES
- 20 LEVERAGE THE BEAM MODEL FOR PROCESSING
- 21 WHERE THE DATA CRUNCHING HAPPENS
- 22 PIPELINE DEEP DIVE
- 23 LEVERAGE THE INTERNAL LOAD BALANCER OF GKE TO GET PREDICTIONS
- 24 DEWARPING THE BOUNDING BOXES TO GET COORDINATES
- 25 TEAM DETECTION WITHOUT BACKGROUND SUBTRACTION
- 26 DUMPING THE PREDICTIONS TO BIGTABLE
- 27 LEVERAGE THE BEAM MODEL TO WINDOW THE DATA
- 28 RESPECT THE BEAM MODEL TO GET DESIRED PARALLELIZATION
- 29 TEST IN STREAM MODE
