Indoor Actions Classification Through Long Short Term Memory Neural Networks

被引：5

作者：

Cipolla, Emanuele ^{[1
]}

Infantino, Ignazio ^{[1
]}

Maniscalco, Umberto ^{[1
]}

Pilato, Giovanni ^{[1
]}

Vella, Filippo ^{[1
]}

机构：

[1] Natl Res Council Italy, ICAR, Via Ugo la Malfa 153, Palermo, Italy

来源：

IMAGE ANALYSIS AND PROCESSING,(ICIAP 2017), PT I | 2017年 / 10484卷

关键词：

Deep learning; Human actions; LSTM; Indoor activities; AMBIENT INTELLIGENCE; ENVIRONMENT; BEHAVIOR;

D O I：

10.1007/978-3-319-68560-1_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work presents a system based on a recurrent deep neural network to classify actions performed in an indoor environment. RGBD and infrared sensors positioned in the rooms are used as data source. The smart environment the user lives in can be adapted to his/her needs.

引用

页码：435 / 444

页数：10

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