Activity Recognition with Finite State Machines
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1348-1352.
Abstract
This paper shows how to learn general, Finite State Machine representations of activities that function as recognizers of previously unseen instances of activities. The central problem is to tell which differences between instances of activities are unimportant and may be safely ignored for the purpose of learning generalized representations of activities. We develop a novel way to find the essential parts of activities by a greedy kind of multiple sequence alignment, and a method to transform the resulting alignments into Finite State Machine that will accept novel instances of activities with high accuracy.
@inproceedings{ijcai2011p228,
title = {Activity Recognition with Finite State Machines},
author = {Kerr, Wesley and Tran, Anh and Cohen, Paul R.},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1348--1352},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-228},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-228},
}
TY - CPAPER TI - Activity Recognition with Finite State Machines AU - Kerr, Wesley AU - Tran, Anh AU - Cohen, Paul R. T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1348 EP - 1352 DO - 10.5591/978-1-57735-516-8/IJCAI11-228 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-228 ER -Copied
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