Paper Info
Title
Baseball Player Behavior Recognition System using Multimodal Features with an Augmented Reality Display on a Smart Glass
Abstract
In this paper, a real-time baseball player behavior recognition system is proposed. By analyzing the sensing signals from the wearable sensors and the skeletons from the depth channel belonging a Kinect camera, the behaviors can be recognized by the proposed system. When a body part is occluded or the depth frames is with motion blur effects in a depth camera, the sensing signals from worn sensors can compensate the recognition capability. In addition, by analyzing the multimodal features obtained from heterogeneous sensors, the recognized results can be displayed on a smart glass with an augmented reality displaying. In this prototype, a player's behavior can be monitored by a coach to assist the advising process in an on-field and off-field baseball playing environment.
Year
DOI
Venue
2019
10.1109/ICMEW.2019.00111
2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
Keywords
Field
DocType
behavior recognition,Kinect,sensor,multimodal features
Computer vision,Smart glass,Computer science,Wearable computer,Motion blur,Communication channel,Augmented reality,Behavior recognition,Artificial intelligence
Conference
ISSN
ISBN
Citations 
2330-7927
978-1-5386-9215-8
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Wei-Chen Yen100.68
Chih-Chieh Fang201.35
Shih-Wei Sun312720.28
Kai-Lung Hua426542.99
Huang-Chia Shih518721.98