Indoor actions classification through long short term memory neural networks
Contributo in Atti di convegno
Data di Pubblicazione:
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.
Tipologia CRIS:
04.01 Contributo in Atti di convegno
Keywords:
Deep Learning; Human Actions; LSTM; Indoor Activities
Elenco autori:
Cipolla, Emanuele; Maniscalco, Umberto; Pilato, Giovanni; Infantino, Ignazio; Vella, Filippo
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