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Detecting Offside Position using 3D Reconstruction

Delbom, Axel LU (2020) In Master's Theses in Mathematical Sciences FMAM05 20201
Mathematics (Faculty of Engineering)
Abstract
With the increased number of technical innovations in soccer and the progress in the field of AI, this thesis investigates the possibilities to detect if a player is in offside position using deep learning and video recordings from soccer games. A method to create a 3D reconstruction of the players in the scene is proposed and evaluated. The arguments for using a 3D reconstruction is that it introduces a volume to the players which hopefully result in increased quality of offside decisions. A system for detecting offside position is proposed consisting of an object detector, a pose estimator, a mesh estimator and a team classification. The position of the players are estimated by minimizing the reprojection error between the 2D pose and... (More)
With the increased number of technical innovations in soccer and the progress in the field of AI, this thesis investigates the possibilities to detect if a player is in offside position using deep learning and video recordings from soccer games. A method to create a 3D reconstruction of the players in the scene is proposed and evaluated. The arguments for using a 3D reconstruction is that it introduces a volume to the players which hopefully result in increased quality of offside decisions. A system for detecting offside position is proposed consisting of an object detector, a pose estimator, a mesh estimator and a team classification. The position of the players are estimated by minimizing the reprojection error between the 2D pose and the 3D pose, further on the reprojection is weighted using the confidence of each predicted 2D joint. The system is able to detect players in offside position with a precision of 60% and a recall of 97%. It is also shown that by weighting the reprojection error the precision of detected players in offside position is increased from 5\% to 60%. It is also shown that reconstructing the scene increases the average F1 score from 0.82 to 0.86. (Less)
Please use this url to cite or link to this publication:
author
Delbom, Axel LU
supervisor
organization
course
FMAM05 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Deep learning, football, offside detection
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3431-2020
ISSN
1404-6342
other publication id
2020:E74
language
English
id
9030362
date added to LUP
2020-10-28 14:00:47
date last changed
2020-10-28 14:00:47
@misc{9030362,
  abstract     = {{With the increased number of technical innovations in soccer and the progress in the field of AI, this thesis investigates the possibilities to detect if a player is in offside position using deep learning and video recordings from soccer games. A method to create a 3D reconstruction of the players in the scene is proposed and evaluated. The arguments for using a 3D reconstruction is that it introduces a volume to the players which hopefully result in increased quality of offside decisions. A system for detecting offside position is proposed consisting of an object detector, a pose estimator, a mesh estimator and a team classification. The position of the players are estimated by minimizing the reprojection error between the 2D pose and the 3D pose, further on the reprojection is weighted using the confidence of each predicted 2D joint. The system is able to detect players in offside position with a precision of 60% and a recall of 97%. It is also shown that by weighting the reprojection error the precision of detected players in offside position is increased from 5\% to 60%. It is also shown that reconstructing the scene increases the average F1 score from 0.82 to 0.86.}},
  author       = {{Delbom, Axel}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Detecting Offside Position using 3D Reconstruction}},
  year         = {{2020}},
}