A Scenario 3: Visual pose tracking Discrete variables: type of movement Continuous variables: head, legs, and torso position Courtesy Pavlovic. J. Rehg, T.-J. Cham, and K. Murphy Hybrid Mode Estimation and Gaussian Filtering with Hybrid HMM Models 16412/6.834 Lecture,15 March2004 Scenarios 1-3: Common properties 1.Continuous dynamics 2. Finite set of behaviors, determines dynamics Continuous state hidden Noisy observations Need continuous statistical estimation Uncertainty in the model Need both System may switch Need to track discrete changes between behaviors Hybrid Mode Estimation and Gaussian Filtering with Hybrid HMM Models 6. 412/6.834 Lecture, 15 March 200Hybrid Mode Estimation and Gaussian Filtering with Hybrid HMM Models 16.412 / 6.834 Lecture, 15 March 2004 7 Scenario 3: Visual pose tracking Discrete variables: type of movement Continuous variables: head, legs, and torso position Courtesy Pavlovic. J. Rehg, T.-J. Cham, and K. Murphy Hybrid Mode Estimation and Gaussian Filtering with Hybrid HMM Models 16.412 / 6.834 Lecture, 15 March 2004 8 Scenarios 1-3: Common properties 1. Continuous dynamics 2. Finite set of behaviors, determines dynamics z Continuous state hidden z Noisy observations z Uncertainty in the model z System may switch between behaviors Need continuous statistical estimation Need to track discrete changes Need both