Semi-automatic tracking of beach volleyball players
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1 Semi-automatic tracking of beach volleyball players Gabriel Gomez, André Linarth, Daniel Link, Bjoern Eskofier Pattern Recognition Lab (CS 5) Digital Sports Group
2 Outline Introduction Methodology Results Summary [Köpke/beach-inside.de]
3 Introduction Videoanalysis in professional sports Technical training Individual training of players Improve technique Reduce errors Analyze habits Tactical training Analyze opposing teams Create specific strategy [ Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 3
4 Introduction German beach volleyball team uses analysis software by the TUM BeachScouter BeachViewer Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 4
5 Introduction BeachScouter Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 5
6 Introduction BeachViewer Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 6
7 Introduction BeachTracker Tracking software complementing the BeachScouter Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 7
8 Outline Introduction Methodology Results Summary [Köpke/beach-inside.de]
![ch/guides/xml/epub/test/ epub-source/flash_tutorials_files/300px- HSV-color-wheel.png] 12.09.](/docs-images/85/92878320/images/9-3.jpg "2012 Dipl.-Ing.")
9 Methodology Particle filter approach Color histogram based Motion based [ ion/motion_theater/ken/strobe-motion-ta-08.jpg] [ epub-source/flash_tutorials_files/300px- HSV-color-wheel.png] Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 9
10 Methodology 4 particle clouds with particles each Set of particles represents Probability Density Function Each particle is weighted hypothesis of player s position Homographic transformation from world coordinates to image coordinates Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 10
11 Methodology Calibration from initial frames of the video Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 11
12 Methodology Each player and each particle with a corresponding bounding box Reference color histograms for each player Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 12
13 Methodology Weight of the particles proportional to histogram similarity BIG WEIGHT small weight PDF Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 13
14 Methodology Bhattacharyya distance used for measuring the similarity of the normalized color histograms Player Sand Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 14
15 Methodology Motion based weighting Particle weight also proportional to number of white pixels in each subwindow Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 15
16 Methodology Combination of color histogram and motion cues for weighting Pweight = Color weight + movement weight Resampling of particles in next frame proportional to particles weights in current frame Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 16
17 Methodology Predicted players position Predicted position as average state of the particle set Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 17
18 Methodology Other measures for improving the tracking Calculation of a background image and comparison of histograms Updating of the reference histogram Restrict particle clouds to certain regions Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 18
19 Outline Introduction Methodology Results Summary [Köpke/beach-inside.de]
20 Results Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 20
21 Results Evaluation on 23 video sequences of ~247 frames each Average number of frames tracked (rounded) Percent of correctly tracked frames Player 1 (front left) % Player 2 (front right) % Player 3 (back right) % Player 4 (back left) % Average players front % Average players back % Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 21
22 Results Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 22
23 Outline Introduction Methodology Results Summary [ Süddeutsche.de/dpa/ebc/]
24 Summary BeachTracker complementing BeachScouter for semiautomatic tracking of the players Color histogram and motion based cues Particle filter approach Good tracking results when homogeneous illumination conditions Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 24
25 Thank you for your attention
26 Bhattacharyya Distance D h ref, h hyp = 1 bεb h b ref h b hyp ref hyp bεb h b bεb h b Where B denotes the number of histogram bins, and h b represents the value of the b-th bin of the reference or hypothesized histogram Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 26
27 Particle Weighting ω j i = c k D i,j + F k Where ω is the weight of the j-th particle, i-th player; k is the number of subregions of the bounding box; D i,j is the Bhattacharyya distance; F is the normalized sum of foreground pixels; c is a constant that balances color and motion cue; Dipl.-Ing. Gabriel Gomez Pattern Recognition Lab (CS 5) Semi-Automatic Tracking of Beach Volleyball Players 27
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