Conditional random fields for activity recognition
| Autor: | John Lafferty, Manuela Veloso, Douglas L. Vail |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Conditional random field
Computer science business.industry Feature extraction Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) Pattern recognition Machine learning computer.software_genre Activity recognition ComputingMethodologies_PATTERNRECOGNITION Discriminative model Pattern recognition (psychology) Artificial intelligence Hidden Markov model business computer Independence (probability theory) |
| Zdroj: | AAMAS |
| DOI: | 10.1145/1329125.1329409 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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