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Learning models of activities involving interacting objects. / Manfredotti, Cristina; Pedersen, Kim Steenstrup; Hamilton, Howard J.; Zilles, Sandra.
Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, Proceedings. red. / Allan Tucker; Frank Höppner; Arno Siebes; Stephen Swift. Springer, 2013. s. 285-297 (Lecture notes in computer science, Bind 8207).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Manfredotti, C
, Pedersen, KS, Hamilton, HJ & Zilles, S 2013,
Learning models of activities involving interacting objects. i A Tucker, F Höppner, A Siebes & S Swift (red),
Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, Proceedings. Springer, Lecture notes in computer science, bind 8207, s. 285-297, 12th International Symposium on Advances in Intelligent Data Analysis, London, Storbritannien,
17/10/2013.
https://doi.org/10.1007/978-3-642-41398-8_25APA
Manfredotti, C.
, Pedersen, K. S., Hamilton, H. J., & Zilles, S. (2013).
Learning models of activities involving interacting objects. I A. Tucker, F. Höppner, A. Siebes, & S. Swift (red.),
Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, Proceedings (s. 285-297). Springer. Lecture notes in computer science Bind 8207
https://doi.org/10.1007/978-3-642-41398-8_25Vancouver
Manfredotti C
, Pedersen KS, Hamilton HJ, Zilles S.
Learning models of activities involving interacting objects. I Tucker A, Höppner F, Siebes A, Swift S, red., Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, Proceedings. Springer. 2013. s. 285-297. (Lecture notes in computer science, Bind 8207).
https://doi.org/10.1007/978-3-642-41398-8_25Author
Manfredotti, Cristina ; Pedersen, Kim Steenstrup ; Hamilton, Howard J. ; Zilles, Sandra. / Learning models of activities involving interacting objects. Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, Proceedings. red. / Allan Tucker ; Frank Höppner ; Arno Siebes ; Stephen Swift. Springer, 2013. s. 285-297 (Lecture notes in computer science, Bind 8207).
Bibtex
@inproceedings{237d389379ae4fcab3c41bf0f3d7ed49,
title = "Learning models of activities involving interacting objects",
abstract = "We propose the LEMAIO multi-layer framework, which makes use of hierarchicalabstraction to learn models for activities involving multiple interacting objectsfrom time sequences of data concerning the individual objects. Experiments in the sea navigation domain yielded learned models that were then successfully applied to activity recognition, activity simulation and multi-target tracking. Our method compares favourably with respect to previously reported results using Hidden Markov Models and Relational Particle Filtering.",
author = "Cristina Manfredotti and Pedersen, {Kim Steenstrup} and Hamilton, {Howard J.} and Sandra Zilles",
year = "2013",
doi = "10.1007/978-3-642-41398-8_25",
language = "English",
isbn = "978-3-642-41397-1",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "285--297",
editor = "Allan Tucker and Frank H{\"o}ppner and Arno Siebes and Stephen Swift",
booktitle = "Advances in Intelligent Data Analysis XII",
address = "Switzerland",
note = "12th International Symposium on Advances in Intelligent Data Analysis, IDA 2013 ; Conference date: 17-10-2013 Through 19-10-2013",
}
RIS
TY - GEN
T1 - Learning models of activities involving interacting objects
AU - Manfredotti, Cristina
AU - Pedersen, Kim Steenstrup
AU - Hamilton, Howard J.
AU - Zilles, Sandra
N1 - Conference code: 12
PY - 2013
Y1 - 2013
N2 - We propose the LEMAIO multi-layer framework, which makes use of hierarchicalabstraction to learn models for activities involving multiple interacting objectsfrom time sequences of data concerning the individual objects. Experiments in the sea navigation domain yielded learned models that were then successfully applied to activity recognition, activity simulation and multi-target tracking. Our method compares favourably with respect to previously reported results using Hidden Markov Models and Relational Particle Filtering.
AB - We propose the LEMAIO multi-layer framework, which makes use of hierarchicalabstraction to learn models for activities involving multiple interacting objectsfrom time sequences of data concerning the individual objects. Experiments in the sea navigation domain yielded learned models that were then successfully applied to activity recognition, activity simulation and multi-target tracking. Our method compares favourably with respect to previously reported results using Hidden Markov Models and Relational Particle Filtering.
U2 - 10.1007/978-3-642-41398-8_25
DO - 10.1007/978-3-642-41398-8_25
M3 - Article in proceedings
SN - 978-3-642-41397-1
T3 - Lecture notes in computer science
SP - 285
EP - 297
BT - Advances in Intelligent Data Analysis XII
A2 - Tucker, Allan
A2 - Höppner, Frank
A2 - Siebes, Arno
A2 - Swift, Stephen
PB - Springer
T2 - 12th International Symposium on Advances in Intelligent Data Analysis
Y2 - 17 October 2013 through 19 October 2013
ER -
