Publications

@inproceedings{vellaClassificationIndoorActions2017,

title = {Classification of Indoor Actions through Deep Neural Networks},

author = { Filippo Vella and Agnese Augello and Umberto Maniscalco and Vincenzo Bentivenga and Salvatore Gaglio},

editor = { Dipanda A. Chbeir R. Gallo L. Yetongnon K. De Pietro G.},

doi = {10.1109/SITIS.2016.22},

isbn = {978-1-5090-5698-9},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016},

pages = {82--87},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. textcopyright 2016 IEEE.},

keywords = {Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vella_classification_2017,

title = {Classification of Indoor Actions through Deep Neural Networks},

author = {Filippo Vella and Agnese Augello and Umberto Maniscalco and Vincenzo Bentivenga and Salvatore Gaglio},

editor = {Chbeir R. Dipanda A. De Pietro G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019213644&doi=10.1109%2fSITIS.2016.22&partnerID=40&md5=329d35941a322add5df73469e33e0f07},

doi = {10.1109/SITIS.2016.22},

isbn = {978-1-5090-5698-9},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016},

pages = {82–87},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. © 2016 IEEE.},

keywords = {Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D},

pubstate = {published},

tppubtype = {inproceedings}

}