RNN-based personalized activity recognition in multi-person environment using RFID군중 환경에서 RFID 정보를 이용한 RNN 기반 개인화 된 활동인식에 관한 연구

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 465
  • Download : 0
Personalized activity recognition, which targets specific single-person in multi-person environments, can be very effective in various settings where each person has their own objects and movement patterns. However, most activity recognition researches only deal with general activity recognition, which uses the same model for all single-person individuals. This is because it is difficult to build a customized model for each individual via manual feature engineering. Thus, in this paper, we introduce personalized activity recognition as a new research direction and propose our own approach to build model for each individual using recurrent neural network (RNN). Also, we suggest a graph-based event processing approach to seamlessly collect time-sliced and annotated data. Finally, we construct three kinds of RNN architectures with three different unit types including iRNN, LSTM and GRU, and perform experiments using real dataset. From the experimental results, we conclude that our approach is feasible to build the customized model in real-world for personalized activity recognition.
Advisors
Kim , Daeyoungresearcher김대영researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2017.2,[iv, 37 p. :]

Keywords

Personalized Activity Recognition; Recurrent Neural Network; Deep Learning; RFID; Object Tracking; 개인화된 활동 인식; 리커런트 뉴럴 네트워크; 딥 러닝; 오브젝트 추적

URI
http://hdl.handle.net/10203/243421
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675472&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0