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Indoor actions classification through long short term memory neural networks

Conference Paper
Publication Date:
2017
abstract:
Thisworkpresentsasystembasedonarecurrentdeepneural 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.
Iris type:
04.01 Contributo in Atti di convegno
Keywords:
Deep Learning; Human Actions; LSTM; Indoor Activities
List of contributors:
Cipolla, Emanuele; Maniscalco, Umberto; Pilato, Giovanni; Infantino, Ignazio; Vella, Filippo
Authors of the University:
INFANTINO IGNAZIO
MANISCALCO UMBERTO
PILATO GIOVANNI
VELLA FILIPPO
Handle:
https://iris.cnr.it/handle/20.500.14243/337789
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