Basketball Pose-based Action Recognition

dc.contributor.author	Zakharchenko, Iryna
dc.date.accessioned	2024-02-15T08:14:46Z
dc.date.available	2024-02-15T08:14:46Z
dc.date.issued	2020
dc.identifier.citation	Zakharchenko, Iryna. Basketball Pose-based Action Recognition /Zakharchenko, Iryna; Supervisor: Orest Varga; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2020. – 32 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4488
dc.language.iso	en	uk
dc.title	Basketball Pose-based Action Recognition	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	Action detection on a team sport is a challenging task, while sports analysis is on-demand and in high interest. A great number of researchers try to make analysis automated. Despite enormous success in image classification using deep learning, action recognition in the video remains a difficult task, and at present no good solu- tion exists in terms of accuracy and speed. The main challenge in action recognition is to design architecture that will capture both spatial and temporal information. In team sports action analysis, the serious challenges are that we have multiple play- ers performing simultaneously different actions, the players are constantly moving, there are occlusions, the camera itself is moving. The proposed method is able to si- multaneously recognize the actions of multiple players using pose estimation, track- ing, and LSTM for action classification.	uk