Combining discriminative features to infer complex trajectories

We propose a new model for the probabilistic estimation of continuous state variables from a sequence of observations, such as tracking the position of an object in video. This mapping is modeled as a product of dynamics experts (features relating the state at adjacent time-steps) and observation experts (features relating the state to the image sequence). Individual features are flexible in that they can switch on or off at each time-step depending on their inferred relevance (or on additional side information), and discriminative in that they need not model the full generative likelihood of the data. When trained conditionally, this permits the inclusion of a broad range of rich features (for example, features relying on observations from multiple time-steps), and allows the relevance of features to be learned from labeled sequences.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: David A. Ross, Simon Osindero, Richard S. Zemel
• Keywords: Continuous State Variables, ICML 2006, Inferred Relevance, Machine Learning, Rich Features