Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions

Pobar, Miran; Ivašić-Kos, Marina

doi:10.1109/CISP-BMEI.2018.8633201

Scientific paper - Original scientific paper

Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions

In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). / Li, W ; Li, Q ; Wang, L (Ed.). IEEE, 2019. pp. 1-6. https://doi.org/10.1109/CISP-BMEI.2018.8633201

Pobar, Miran; Ivašić-Kos, Marina

Cite this document

APA 6th Edition

Pobar, M. & Ivašić Kos, M. (2019). Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions. In W. Li, (Ed.), Q. Li, (Ed.), L. Wang, (Ed.), 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1-6). IEEE. doi: 10.1109/CISP-BMEI.2018.8633201

MLA 8th Edition

Pobar, Miran and Marina Ivašić Kos. "Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions." 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), edited by W Li, edited by Q Li, edited by L Wang, IEEE, 2019, pp. 1-6. https://urn.nsk.hr/urn:nbn:hr:195:399766

Chicago 17th Edition

Pobar, Miran and Marina Ivašić Kos. "Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions." In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 1-6. IEEE, 2019. Accessed 2024 November 22. https://urn.nsk.hr/urn:nbn:hr:195:399766

Harvard

Pobar, M. and Ivašić Kos, M. (2019) 'Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions', in Li, W. (ed.), Li, Q. (ed.), Wang, L. (ed.), 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE, pp. 1-6. Available at: https://urn.nsk.hr/urn:nbn:hr:195:399766 (Accessed 22 November 2024)

Vancouver

Pobar M, Ivašić Kos M. Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions. In: W. Li, ed., Q. Li, ed., L. Wang, ed. 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE; 2019. Pp. 1-6. [cited 2024 November 22] Available at: https://urn.nsk.hr/urn:nbn:hr:195:399766

IEEE

M. Pobar and M. Ivašić Kos, "Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions", 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), W. Li, Q. Li and L. Wang, Eds. IEEE, 2019. [Online] Available at: https://urn.nsk.hr/urn:nbn:hr:195:399766 [Accessed: 22 November 2024]

Cite this item: https://urn.nsk.hr/urn:nbn:hr:195:399766

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Metadata

Title (english)	Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions
Author	Miran Pobar https://orcid.org/0000-0001-5604-2128
Author	Marina Ivašić-Kos https://orcid.org/0000-0002-1940-5089
Editor	W Li
Editor	Q Li
Editor	L Wang
Author's institution	University of Rijeka (Faculty of Informatics and Digital Technologies)
Scientific / art field, discipline and subdiscipline	SOCIAL SCIENCES Information and Communication Sciences
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing Artificial Intelligence
Abstract (english)	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.
Keywords (english)
Language	english
Publication type	Scientific paper - Original scientific paper
Publication status	Published
Peer review	Peer review - international
Publication version	Accepted version
Publication title	2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Numbering	pp. 1-6
ISBN	978-1-5386-7604-2 (Electronic ISBN)
ISBN	978-1-5386-7605-9 (Print on Demand(PoD) ISBN)
DOI	https://doi.org/10.1109/CISP-BMEI.2018.8633201
URN:NBN	urn:nbn:hr:195:399766
Publication	2019-02
Project	Number: IP-2016-06-8345 Title (croatian): Automatsko raspoznavanje akcija i aktivnosti u multimedijalnom sadržaju iz domene sporta Title (english): Automatic recognition of actions and activities in multimedia content from sports domain Acronym: RAASS Leader: Marina Ivašić Kos Jurisdiction: Croatia Funding stream: IP
Conference	Title: 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics Acronym: CISP-BMEI Location: Peking, China Start date: 2018-10-13 End date: 2018-10-15 Assembly type: Lecture
Document URL	https://ieeexplore.ieee.org/document/8633201
Type of resource	Text
Publisher	IEEE
Access conditions	Open access
Terms of use
Public note	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Created on	2023-02-12 20:14:08