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Background Segmentation Methods in Analysis of Live Sport Video Recordings

Hammar, Fredrik LU and Flinke, Johan LU (2018) In Master's Theses in Mathematical Sciences FMAM05 20181
Mathematics (Faculty of Engineering)
Abstract
A sports video analysis application was developed by Spiideo for mobile devices. It presents recordings from practices and competitive games for game-play analysis. Available tools, such as on-screen drawings, require a robust background foreground segmentation. The currently used segmentation method have difficulties to master the shifting conditions in weather, shadows and shirt colors.

The purpose of this project was to improve the background foreground segmentation in the application by evaluating alternative methods and examine possible improvements of the currently used method.

A data-set with recordings from Spiideo clients was created and used to evaluate and compare different segmentation methods with the current method.... (More)
A sports video analysis application was developed by Spiideo for mobile devices. It presents recordings from practices and competitive games for game-play analysis. Available tools, such as on-screen drawings, require a robust background foreground segmentation. The currently used segmentation method have difficulties to master the shifting conditions in weather, shadows and shirt colors.

The purpose of this project was to improve the background foreground segmentation in the application by evaluating alternative methods and examine possible improvements of the currently used method.

A data-set with recordings from Spiideo clients was created and used to evaluate and compare different segmentation methods with the current method. The evaluation included pixel classification scores, complexity measurements and a visual evaluation.

A median background model frame difference approach showed better performance with lower computational time compared to what is currently used. A Mixture of Gaussians method gave the best pixel classification result, but increased the calculation time. Suggested alterations of the currently used method also showed minor improvements in performance. (Less)
Please use this url to cite or link to this publication:
author
Hammar, Fredrik LU and Flinke, Johan LU
supervisor
organization
course
FMAM05 20181
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Image Analysis, Background Foreground Segmentation, Segmentation Evaluation, Live Sports Video Analysis
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3358-2018
ISSN
1404-6342
other publication id
2018:E40
language
English
id
8948473
date added to LUP
2018-06-19 16:21:08
date last changed
2018-06-19 16:21:08
@misc{8948473,
  abstract     = {A sports video analysis application was developed by Spiideo for mobile devices. It presents recordings from practices and competitive games for game-play analysis. Available tools, such as on-screen drawings, require a robust background foreground segmentation. The currently used segmentation method have difficulties to master the shifting conditions in weather, shadows and shirt colors. 

The purpose of this project was to improve the background foreground segmentation in the application by evaluating alternative methods and examine possible improvements of the currently used method.

A data-set with recordings from Spiideo clients was created and used to evaluate and compare different segmentation methods with the current method. The evaluation included pixel classification scores, complexity measurements and a visual evaluation.

A median background model frame difference approach showed better performance with lower computational time compared to what is currently used. A Mixture of Gaussians method gave the best pixel classification result, but increased the calculation time. Suggested alterations of the currently used method also showed minor improvements in performance.},
  author       = {Hammar, Fredrik and Flinke, Johan},
  issn         = {1404-6342},
  keyword      = {Image Analysis,Background Foreground Segmentation,Segmentation Evaluation,Live Sports Video Analysis},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Background Segmentation Methods in Analysis of Live Sport Video Recordings},
  year         = {2018},
}