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3D articulated human motion tracking using PLS-RBPF
2007-05-12
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Today, Professor Gang Qian called me and discussed with me several questions regarding my work on 3D articulated tracking. He has several good points. I think the discussion with him also stimulate me to think deeply about the advantage of PLS-RBPF and why it can be called RBPF inference.He pointed the following points:1. The Kalman prediction using correlation B effectively restrict the samples only to be draw from the temproal dynamics that conforms well to human motion, eliminating samples that does not conform to human motion structure. 2. Dr. Qian thinks that until weighting each sample by the image likilihood, we are not in the RBPF tracking framework, because he thinks that until the "weighting" step, no true measurement is incorporated. I have convinced him that we are in the RBPF tracking framework because of the Kalman Update step which incorporate the true measurement through the auxiliry measurement Ot-1 that is the leaf mean state at the previous time step.3. Dr. Qian thinks that our movement modeling method of learning the correlation using PLS regression is more general in the sense that as long as there are correlations in the joint angle data, we can then learn this from the training data and apply it to tracking. Thus our method is able to track other kinds of motions like jogging, running or even dancing motion sequence.
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