Learning and classification of complex dynamics

Journal article

Standard, exact techniques based on likelihood maximization are available for learning Auto-Regressive Process models of dynamical processes. The uncertainty of observations obtained from real sensors means that dynamics can be observed only approximately. Learning can still be achieved via 'EM-K'-Expectation-Maximization (EM) based on Kalman Filtering. This cannot handle more complex dynamics, however, involving multiple classes of motion. A problem arises also in the case of dynamical processes observed visually: background clutter arising for example, in camouflage, produces non-Gaussian observation noise. Even with a single dynamical class, non-Gaussian observations put the learning problem beyond the scope of EM-K. For those cases, we show here how 'EM-C' - based on the Condensation algorithm which propagates random 'particle-sets', can solve the learning problem. Here, learning in clutter is studied experimentally using visual observations of a hand moving over a desktop. The resulting learned dynamical model is shown to have considerable predictive value: When used as a prior for estimation of motion, the burden of computation in visual observation is significantly reduced. Multiclass dynamics are studied via visually observed juggling; plausible dynamical models have been found to emerge from the learning process, and accurate classification of motion has resulted. In practice, EM-C learning is computationally burdensome and the paper concludes with some discussion of computational complexity.

Authors: North, B; Blake, A; Isard, M; Rittscher, J

• Publication status: Published
• Publisher: IEEE
• Journal: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
• Volume: 22
• Issue: 9
• Pages: 1016-1034
• Publication date: 2000-09-01
• DOI: 10.1109/34.877523
• ISSN: 0162-8828
• Language: English
• Keywords: Auto-Regressive Process; Expectation Maximization; computer vision; learning dynamics