Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features

Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features

In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players’ behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper...

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Bibliographic Details
Main Authors: Shih-Wei Sun, Ting-Chen Mou, Chih-Chieh Fang, Pao-Chi Chang, Kai-Lung Hua, Huang-Chia Shih
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Sensors
Subjects:
behavior recognition
multimodal
machine learning
deep learning
LSTM network
depth camera
inertial sensor
Online Access:https://www.mdpi.com/1424-8220/19/6/1425

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