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Live video streaming and analysis in indoor soccer with a quadcopter

Live video streaming and analysis in indoor soccer with a quadcopter. Filipe Trocado Ferreira MSc Electrical Engineering-Automation Jaime Cardoso ( PhD ) Advisor Hélder Oliveira ( PhD ) Co-Advisor. Motivation. Live Soccer Analysis Big Investments in technical and tactical preparation

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Live video streaming and analysis in indoor soccer with a quadcopter

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  1. Live video streaming and analysis in indoor soccer with a quadcopter Filipe Trocado Ferreira MScElectricalEngineering-Automation Jaime Cardoso (PhD) Advisor Hélder Oliveira (PhD) Co-Advisor

  2. Motivation • Live Soccer Analysis • Big Investments in technical and tactical preparation • Individual and team performance data collected from video • The Problem • Complex implementation • Still a lot of human intervention • System not suitable for the big majority of the teams • Our Solution • Low cost architecture based on an unnamed air vehicle with a camera onboard

  3. MainGoals • Videosequencesfrom Indoor Soccer Games usinganAr.Drone 2.0 • Collect and Filtering low-level data as: • Players and Ball positions and trajectories in both image and world coordinate system • Team Identification • Frame-to-Frame and Image-to-Pitch transformations • Occlusion detection and resolution • High-Level data: • Ball Possession • Field Occupation • Offensive/Defensive trends • … • Flight Control Architecture for Automatic Image Recording (optional Goal): • Position Stabilization • Ball following and avoidance

  4. Initial Framework Motion Compensation Video Sequences Raw Player coordinates Stable Images Camera Calibration Player and ball Detection Calibrated Images H High-Level Data Interpretation Temporal and Spatial Filtering Player Coordinates

  5. Videosequences • Imagesequencesrecordedbyan Ar.Drone 2.0 (manual flight control) • Main issues: • Camera stabilization and motion compensation

  6. CameraStabilization • Calculationof Frame-to-Frame affinetransformation • UsingPointFeatureMatching (FAST Features) • Mainissues: • Can nothandlebigoscillations

  7. CameraCalibration • Correctionof “Barrel” Effect • Calculationoftheperspectivetrasnformation (min. of 4 pairofpointsrequested) • Mainissues: • Long termdrift • Thereistheneedof a periodicperspectiverecalibration • Needof a precise geometricmodel for eachplayground

  8. PlayerDetection • Non static background and a lot of lines of different colors on the playground do not allow basic segmentation methods (as background subtraction or color segmentation) • HOG people detection: Histogram Oriented Gradients features and a trained SVM classifier for detecting players in an upright pose. • Main Issues: • Fails when players are running, tackling and in cases of occlusions. • Low False Positives but a lot of missed Positives

  9. Some Raw Results…

  10. What comes next? • Spatial and Temporal Filtering: • False Positive handling • Dynamic Model (Kalman Filter/Particles Filter) • Team/Player Identification • Initial Framework Improvements: • Dynamic Calibration • Creation of a SVM classifier for HOG player detector • Ground truth annotation • High-Level Data Interpretation: • Basic Statistics with filtered player positions • Complex Team Information • Relation with players and ball possession

  11. Time to shoot some questions!

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