Artificial Intelligence for Sport Actions and Performance Analysis using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM)

被引：5

作者：

Fok, Wilton W. T. ^{[1
]}

Chan, Louis C. W. ^{[1
]}

Chen, Carol ^{[1
]}

机构：

[1] Univ Hong Kong, Hong Kong, Peoples R China

来源：

ICRAI 2018: PROCEEDINGS OF 2018 4TH INTERNATIONAL CONFERENCE ON ROBOTICS AND ARTIFICIAL INTELLIGENCE - | 2018年

关键词：

Artificial Intelligence; Sport Performance Analysis; Deep Learning; RNN; LSTM; Human Activity Recognition;

D O I：

10.1145/3297097.3297115

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The development of Human Action Recognition (HAR) system is getting popular. This project developed a HAR system for the application in the surveillance system to minimize the man-power for providing security to the citizens such as public safety and crime prevention. In this research, deep learning network using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are used to analyze dynamic video motion of sport actions and classify different types of actions and their performance. It could classify different types of human motion with a small number of video frame for efficiency and memory saving. The current accuracy achieved is up to 92.9% but with high potential of further improvement.

引用

页码：40 / 44

页数：5

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