Robust Head Pose Estimation Using Textured Polygonal Model with Local Correlation Measure
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A testbed for studying and choosing predictive tracking algorithms in virtual environments
EGVE '03 Proceedings of the workshop on Virtual environments 2003
Double exponential smoothing: an alternative to Kalman filter-based predictive tracking
EGVE '03 Proceedings of the workshop on Virtual environments 2003
Head orientation prediction: delta quaternions versus quaternions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiresolution-based bilinear recurrent neural network
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Object tracking based on parzen particle filter using multiple cues
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
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The use of head movements in control applications leaves the hands free for other tasks and utilizes the mobility of the head to acquire and track targets over a wide field of view. We present the results of applying a Kalman filter to generate prediction estimates for tracking head positions. A simple kinematics approach based on the assumption of a piecewise constant acceleration process is suggested and is shown to track head positions with an rms error under 2° for head movements with accelerations smaller than 3000°/s. To account for the wide range of head dynamic characteristics, an adaptive approach with input estimation is developed. The performance of the Kalman filter is compared to that based on a simple polynomial predictor