live video streaming and analysis in indoor soccer with a quadcopter
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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 - PowerPoint PPT PresentationTRANSCRIPT
Live video streaming and analysis in indoor soccer with a quadcopter
Filipe Trocado FerreiraMSc Electrical Engineering-Automation
Jaime Cardoso (PhD)Advisor
Hélder Oliveira (PhD)Co-Advisor
MotivationLive 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
Main Goals Video sequences from Indoor Soccer Games using an Ar.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
Initial Framework
Video Sequences
Motion Compensation
Stable Images
Camera CalibrationH
Calibrated Images
Player and ballDetection
Temporal and SpatialFiltering
Raw Player coordinates
Player Coordinates
High-Level Data Interpretation
Video sequences Image sequences recorded by an Ar.Drone 2.0 (manual
flight control)
Main issues:◦ Camera stabilization and motion compensation
Camera Stabilization Calculation of Frame-to-Frame affine transformation Using Point Feature Matching (FAST Features)
Main issues:◦ Can not handle big oscillations
Camera Calibration Correction of “Barrel” Effect Calculation of the perspective trasnformation (min. of 4 pair of
points requested)
Main issues:◦ Long term drift
◦ There is the need of a periodic perspective recalibration
◦ Need of a precise geometric model for each playground
Player Detection 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
Some Raw Results…
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 annotationHigh-Level Data Interpretation:
◦ Basic Statistics with filtered player positions◦ Complex Team Information◦ Relation with players and ball possession
Time to shoot some questions!