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Computational Intelligence
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UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Use of the Gibbs sampler in expert systems
Artificial Intelligence
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ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
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Artificial Intelligence
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AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
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Introduction to Bayesian Networks
Introduction to Bayesian Networks
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UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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Journal of Ambient Intelligence and Smart Environments
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In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of teh well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.