Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions (CROSBI ID 672478)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Pobar, Miran ; Ivašić-Kos, Marina
engleski
Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions
To build a successful supervised learning model for action recognition a large amount of training data needs to be labeled first. Labeling is normally done manually and it is a tedious and time-consuming task, especially in the case of video footage, when each individual athlete performing a given action should be labeled. To minimize the manual labor, we propose a Mask R-CNN and Optical flow based method to determine the active players who perform a given action among all players presented on the scene. The Mask R-CNN is a deep learning object recognition method used for player detection and optical flow measures player activity. Combining both methods ensures tracking and labeling of active players in handball video sequences. The method was successfully tested on a dataset of handball practice videos recorded in the wild.
object detectors ; sports scenes ; Mask R-CNN ; optical flow ; action recognition database
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
8633201
2018.
objavljeno
10.1109/cisp-bmei.2018.8633201
Podaci o matičnoj publikaciji
2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Li, W ; Li, Q ; Wang, L
Institute of Electrical and Electronics Engineers (IEEE)
978-1-5386-7604-2
Podaci o skupu
11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI 2018)
predavanje
13.10.2018-15.10.2018
Peking, Kina
Povezanost rada
Informacijske i komunikacijske znanosti, Računarstvo