The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
Abstract
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. This paper describes how to extract a person’s activities and significant places from traces of GPS data. The system uses hierarchically structured conditional random fields to generate a consistent model of a person’s activities and places. In contrast to existing techniques, this approach takes the high-level context into account in order to detect the significant places of a person. Experiments show significant improvements over existing techniques. Furthermore, they indicate that the proposed system is able to robustly estimate a person’s activities using a model that is trained from data collected by other persons.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486 · cited 1×
“The BP algorithm was originally proposed in the context of Bayesian networks (Pearl 1988) and was formulated equivalently in models such as factor graphs (Kschischang et al. 2001) and Markov networks (including CRFs) (Yedidia et al. 2001).”
MethodRichardson, M., & Domingos, P. (2006) · Machine Learning · doi:10.1007/s10994-006-5833-1 · Cited by 2,676
Learning Motion Patterns of People for Compliant Robot Motion
Bennewitz, M., Burgard, W., Cielniak, G., & Thrun, S. (2005) · The International Journal of Robotics Research · doi:10.1177/0278364904048962 · Cited by 399
Cited by
A survey of mobile phone sensing
Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5560598 · Cited by 2,292
Unstructured human activity detection from RGBD images
Sung, J., Ponce, C., Selman, B., & Saxena, A. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224591 · Cited by 511
Conditional random fields for activity recognition
Vail, D. L., Veloso, M. M., & Lafferty, J. D. (2007) · Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems · doi:10.1145/1329125.1329409 · Cited by 357
Energy-accuracy trade-off for continuous mobile device location
Lin, K., Kansal, A., Lymberopoulos, D., & Zhao, F. (2010) · Proceedings of the 8th international conference on Mobile systems, applications, and services · doi:10.1145/1814433.1814462 · Cited by 277
Ontology‐based activity recognition in intelligent pervasive environments
Chen, L., & Nugent, C. (2009) · International Journal of Web Information Systems · doi:10.1108/17440080911006199 · Cited by 253
Metadata
- Authors
- Liao, L., Fox, D., & Kautz, H.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2007-01
- Volume
- 26
- Issue
- 1
- Pages
- 119-134
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2171805028
- Cited by
- 412
- References
- 5
- FWCI
- 41.89
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields. https://app.cochat.ai/papers/10.1177/0278364907073775
- The paper
- Liao, L., Fox, D., & Kautz, H. (2007). Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields. The International Journal of Robotics Research. https://doi.org/10.1177/0278364907073775
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
