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Coupled hidden Markov models for complex action recognition
Abstract
We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm and a clear Bayesian semantics. However the Markovian framework makes strong restrictive assumptions about the system generating the signal-that it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.
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Cited by
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Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2198883 · Cited by 1,074
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Self-citationLayered representations for human activity recognition
Oliver, N., Horvitz, E., & Garg, A. (2003) · Proceedings. Fourth IEEE International Conference on Multimodal Interfaces · doi:10.1109/icmi.2002.1166960 · Cited by 312
Self-citationCoupled Hidden Semi Markov Models for Activity Recognition
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Asher, L., Collins, L. M., Ortiz-Pelaez, A., Drewe, J. A., Nicol, C. J., & Pfeiffer, D. U. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2009.0221 · Cited by 102
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Vehicle motion intention reasoning using cooperative perception on urban road
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Metadata
- Authors
- Brand, M., Oliver, N., & Pentland, A.
- Venue
- Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-22
- Pages
- 994-999
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2152239535
- Cited by
- 1,052
- References
- 0
- FWCI
- 34.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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