Conditional random fields for activity recognition
Abstract
Activity recognition is a key component for creating intelligent, multi-agent systems. Intrinsically, activity recognition is a temporal classification problem. In this paper, we compare two models for temporal classification: hidden Markov models (HMMs), which have long been applied to the activity recognition problem, and conditional random fields (CRFs). CRFs are discriminative models for labeling sequences. They condition on the entire observation sequence, which avoids the need for independence assumptions between observations. Conditioning on the observations vastly expands the set of features that can be incorporated into the model without violating its assumptions. Using data from a simulated robot tag domain, chosen because it is multi-agent and produces complex interactions between observations, we explore the differences in performance between the discriminatively trained CRF and the generative HMM. Additionally, we examine the effect of incorporating features which violate independence assumptions between observations; such features are typically necessary for high classification accuracy. We find that the discriminatively trained CRF performs as well as or better than an HMM even when the model features do not violate the independence assumptions of the HMM. In cases where features depend on observations from many time steps, we confirm that CRFs are robust against any degradation in performance.
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After CRFs had been shown to outperform HMMs in other areas, they were applied to activity recognition as well.
“After CRFs had been shown to outperform HMMs in other areas [6], they were applied to activity recognition as well [25,26].”
From Activity recognition using semi-Markov models on real world smart home datasets (2010) · cites this paper as [26]
Previous work on activity recognition shows that most recognition confusion occurs between activities taking place in the same room using the same set of sensors.
“Previous work on activity recognition shows that most recognition confusion occurs between activities taking place in the same room using the same set of sensors [26].”
From Activity recognition using semi-Markov models on real world smart home datasets (2010) · cites this paper as [26]
Spatio-temporal graphs are a popular general tool for representing high-level spatio-temporal structures.
“Spatio-temporal graphs (st-graphs) are a popular [39,37,4,11,32,65,22] general tool for representing such high-level spatio-temporal structures.”
From Structural-RNN: Deep Learning on Spatio-Temporal Graphs (2016) · cites this paper as [11]
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- Authors
- Vail, D. L., Veloso, M. M., & Lafferty, J. D.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
- Publisher
- ACM
- Published
- 2007-05-14
- Pages
- 1-8
- ISSN
- 1548-8403 (ISSN-L)
- OpenAlex
- W2123277412
- Cited by
- 357
- References
- 117
- Citations by year
- 2025: 5 · 2024: 9 · 2023: 6 · 2022: 7 · 2021: 9 · 2020: 44 · 2019: 15 · 2018: 23 · 2017: 23 · 2016: 29 · 2015: 34 · 2014: 30 · 2013: 23 · 2012: 27
- FWCI
- 12.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Adaptive Agents and Multi-Agents Systems (publishedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- Funders
- U.S. Department of the Interior (NBCHD030010)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- activity recognition, conditional random fields, hidden Markov models, temporal classification, multi-agent systems, feature dependencies, sequence labeling
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 ›
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