Capturing the Sporting Heroes of Our Past by Extracting 3D Movements from Legacy Video Content
Egileak: Jon Goenetxea, Luis Unzueta, María Teresa Linaza, Mikel Rodriguez, Noel E. OConnor, Kieran Moran
Data: 03.10.2014
Abstract
Sports are a key part of cultural identity, and it is necessary to preserve them as important intangible Cultural Heritage, especially the human motion techniques specific to individual sports. In this paper we present a method for extracting 3D athlete motion from video broadcast sources, providing an important tool for preserving the heritage represented by these movements. Broadcast videos include camera motion, multiple player interaction, occlusions and noise, presenting significant challenges to solve the reconstruction. The approach requires initial definition of some key-frames and setting of 2D key-points in those frames manually. Thereafter an automatic process estimates the poses and positions of the players in the key-frames, and in the frames between key-frames, taking into account collisions with the environment and human kinematic constraints. Initial results are extremely promising and this data could be used to analyze the sport's evolution over time, or even to generate animations for interactive applications.
BIB_text
author = {Jon Goenetxea, Luis Unzueta, María Teresa Linaza, Mikel Rodriguez, Noel E. OConnor, Kieran Moran},
title = {Capturing the Sporting Heroes of Our Past by Extracting 3D Movements from Legacy Video Content},
pages = {48-58},
volume = {8740},
keywds = {
Motion capture, human body posing, intangible cultural heritage, video legacy.
}
abstract = {
Sports are a key part of cultural identity, and it is necessary to preserve them as important intangible Cultural Heritage, especially the human motion techniques specific to individual sports. In this paper we present a method for extracting 3D athlete motion from video broadcast sources, providing an important tool for preserving the heritage represented by these movements. Broadcast videos include camera motion, multiple player interaction, occlusions and noise, presenting significant challenges to solve the reconstruction. The approach requires initial definition of some key-frames and setting of 2D key-points in those frames manually. Thereafter an automatic process estimates the poses and positions of the players in the key-frames, and in the frames between key-frames, taking into account collisions with the environment and human kinematic constraints. Initial results are extremely promising and this data could be used to analyze the sport's evolution over time, or even to generate animations for interactive applications.
}
isbn = {978-3-319-13694-3},
isi = {1},
date = {2014-10-03},
year = {2014},
}
