nips nips2005 nips2005-121 nips2005-121-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Lin Liao, Dieter Fox, Henry Kautz
Abstract: Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies the significant locations of a person and takes the highlevel context into account. Our system uses relational Markov networks to represent the hierarchical activity model that encodes the complex relations among GPS readings, activities and significant places. We apply FFT-based message passing to perform efficient summation over large numbers of nodes in the networks. We present experiments that show significant improvements over existing techniques. 1
[1] D. Ashbrook and T. Starner. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5), 2003.
[2] E. Oran Brigham. Fast Fourier Transform and Its Applications. Prentice Hall, 1988.
[3] V. Gogate, R. Dechter, C. Rindt, and J. Marca. Modeling transportation routines using hybrid dynamic mixed networks. In Proc. of the Conference on Uncertainty in Artificial Intelligence, 2005.
[4] S. Kumar and M. Hebert. Discriminative random fields: A discriminative framework for contextual interaction in classification. In Proc. of the International Conference on Computer Vision, 2003.
[5] J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. of the International Conference on Machine Learning, 2001.
[6] L. Liao, D. Fox, and H. Kautz. Learning and inferring transportation routines. In Proc. of the National Conference on Artificial Intelligence, 2004.
[7] L. Liao, D. Fox, and H. Kautz. Hierarchical conditional random fields for GPS-based activity recognition. In Proc. of the 12th International Symposium of Robotics Research (ISRR), 2005.
[8] L. Liao, D. Fox, and H. Kautz. Location-based activity recognition using relational Markov networks. In Proc. of the International Joint Conference on Artificial Intelligence, 2005.
[9] Yongyi Mao, Frank R. Kschischang, and Brendan J. Frey. Convolutional factor graphs as probabilistic models. In Proc. of the Conference on Uncertainty in Artificial Intelligence, 2004.
[10] B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In Proc. of the Conference on Uncertainty in Artificial Intelligence, 2002.