Journal Article10.1162/089976699300016890
Modeling and prediction of human behavior
Alex Pentland,Andrew Liu +1 more
TL;DR: This work proposes that many human behaviors can be accurately described as a set of dynamic models sequenced together by a Markov chain and uses these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time.
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Abstract: We propose that many human behaviors can be accurately described as a set of dynamic modes (e.g., Kalman filters) sequenced together by a Markov chain. We then use these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time. To test the power of this modeling approach, we report an experiment in which we were able to achieve 95% accuracy at predicting automobile drivers' subsequent actions from their initial preparatory movements. Language: en
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References
New Results in Linear Filtering and Prediction Theory
R. E. Kalman,R. S. Bucy +1 more
TL;DR: The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results and properties of the variance equation are of great interest in the theory of adaptive systems.
6.9K
An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
A Bayesian computer vision system for modeling human interactions
TL;DR: A real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task and demonstrates the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.
Contour Tracking by Stochastic Propagation of Conditional Density
Michael Isard,Andrew Blake +1 more
- 15 Apr 1996
TL;DR: The Condensation algorithm combines factored sampling with learned dynamical models to propagate an entire probability distribution for object position and shape, over time, and is markedly superior to what has previously been attainable from Kalman filtering.
1.3K
•Dissertation
Visual Recognition of American Sign Language Using Hidden Markov Models.
Thad Starner
- 01 Feb 1995
TL;DR: Using hidden Markov models (HMM's), an unobstrusive single view camera system is developed that can recognize hand gestures, namely, a subset of American Sign Language (ASL), achieving high recognition rates for full sentence ASL using only visual cues.
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