The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Hidden Conditional Random Fields for Gesture Recognition
Abstract
We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models.
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Cites
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Cited by
Hand gesture recognition with 3D convolutional neural networks
Molchanov, P., Gupta, S., Kim, K., & Kautz, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301342 · Cited by 543
Conditional random fields for activity recognition
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Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Jain, A., Koppula, H. S., Raghavan, B., Soh, S., & Saxena, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.364 · Cited by 274
Multimodal Gesture Recognition Based on the ResC3D Network
Miao, Q., Li, Y., Ouyang, W., Ma, Z., Xu, X., Shi, W., & Cao, X. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.360 · Cited by 168
Walecki, R., Rudovic, O., Pavlovic, V., & Pantic, M. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7163137 · Cited by 65
Metadata
- Authors
- Wang, S. B., Quattoni, A., Morency, L.-P., Demirdjian, D., & Darrell, T.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Pages
- 1521-1527
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2145835757
- Cited by
- 451
- References
- 21
- FWCI
- 16.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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