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CVPR 2014 Open Access Repository
Source: cv-foundation.org
Article summary:
1. This article proposes a hierarchical dynamic framework for extracting high-level skeletal joints features and using them to estimate emission probability in order to infer action sequences.
2. Gaussian mixture models are the dominant technique for modeling the emission distribution of hidden Markov models, but this article suggests that deep neural networks can be used instead to achieve better action recognition using skeletal features.
3. The framework can be extended to include an ergodic state to segment and recognize actions simultaneously.
Article analysis:
The article is generally reliable and trustworthy, as it provides a detailed description of the proposed hierarchical dynamic framework for extracting high-level skeletal joints features and using them to estimate emission probability in order to infer action sequences. The authors also provide evidence for their claims by citing related material such as a PDF file and BibTeX entry. Furthermore, they discuss how the framework can be extended to include an ergodic state to segment and recognize actions simultaneously, which further supports their claims.
The only potential bias in the article is that it does not explore any counterarguments or present both sides equally when discussing the use of deep neural networks versus gaussian mixture models for modeling the emission distribution of hidden Markov models. However, this is likely due to space constraints rather than any intentional bias on behalf of the authors.
In conclusion, this article is generally reliable and trustworthy, with no major issues regarding trustworthiness or reliability identified during this review.