Building a labeled dataset for recognition of handball actions using mask R-CNN and STIPS (CROSBI ID 672480)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Ivašić-Kos, Marina ; Pobar, Miran
engleski
Building a labeled dataset for recognition of handball actions using mask R-CNN and STIPS
Building successful machine learning models depends on large amounts of training data that often needs to be labelled manually. We propose a method to efficiently build an action recognition dataset in the handball domain, focusing on minimizing the manual labor required to label the individual players performing the chosen actions. The method uses existing deep learning object recognition methods for player detection and combines the obtained location information with a player activity measure based on spatio-temporal interest points to track players that are performing the currently relevant action, here called active players. The method was successfully used on a challenging dataset of real-world handball practice videos, where the leading active player was correctly tracked and labeled in 84 % of cases.
object detectors , sports scenes , Mask R-CNN , spatio-temporal interest point-STIP , action recognition database
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
1-6.
2018.
objavljeno
10.1109/euvip.2018.8611642
Podaci o matičnoj publikaciji
2018 7th European Workshop on Visual Information Processing (EUVIP)
Institute of Electrical and Electronics Engineers (IEEE)
978-1-5386-6897-9
2471-8963
Podaci o skupu
7th European Workshop on Visual Information Processing (EUVIP 2018)
predavanje
26.11.2018-28.11.2018
Tampere, Finska
Povezanost rada
Računarstvo, Informacijske i komunikacijske znanosti