B. Stenger, P. Mendonca, and R. Cipolla, "Model-- based hand tracking using an unscented kalman filter, " in British Machine Vision Conference, September 2001, vol. 1, pp. 63--72.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... shown to be a superior alternative to the EKF in a variety of applications including state estimation for road vehicle navigation[8] parameter estimation for time series modeling[9] and neural network training[10] The UKF is also effective in certain types of visual contour hand tracking[11][12]. However, these systems dealt mostly with tracking position and did not take orientation into account. Although the UKF has been applied to a wide range of estimation problems, to the best of our knowledge there has been no attempt to use it to improve human head or hand orientation tracking. ....

Stenger, B., P. R. S. Mendonca, and R. Cipolla. Model-Based Hand Tracking Using an Unscented Kalman Filter. In Proceedings of the British Machine Vision Conference, 63-72, September 2001.

....genetic algorithms and least median squares in order to track a 21 dof hand with a single camera. However, their results seem no more precise than others, and finger tips occlusion is still a problem because of the inverse kinematics computing. Finally, the recent paper of Stenger et al. [12] describes a software 3D engine that projects their quadric made hand model into the image plane while keeping information about fingers that are projected on the same pixels. On a standard PC, their system is able to track reliably a hand moving along 6 dof at 3 Hz. But it is unsure how it will ....

Stenger, B., Mendonca, P., Cipolla, R.: Model--based hand tracking using an unscented kalman filter. In: British Machine Vision Conference. Volume 1. (2001) 63--72

....and analysis of biological motion have become active research topics in recent years. A common approach to this task is to model the body as a kinematic tree, and reformulate the problem as an articulated body tracking[8] Most of the state of the art systems rely on predefined kinematic models [21, 20, 18]. Some methods require manual initialization, while other use heuristics [15, 7] or predefined protocols [13] to adapt the model to observations. We are interested in a principled way to recover articulated models from observations. The recovered models may then be used for further tracking ....

....structured as follows: in Section 2 we describe relevant prior work, we then describe the probabilistic formulation in Section 3, and finally we present the algorithm used for computations (Section 4) our experiments and the conclusions. 2. Prior Work While state of the art tracking algorithms [21, 9, 5, 20, 18] do not address either model creation or model initialization, the necessity of automating these two steps has been long recognized. The approach in [13] required a subject to follow a set of predefined movements, and recovered the descriptions of body parts and body topology from deformations of ....

B. Stenger, P. R. S. Mendonca, and R. Cipolla. Model-based hand tracking using an unscented kalman filter. Proc. British Machine Vision Conference, 2001.

....gives an overview of the tracking system. The major contributions of this initial work are the use of a hand model which can be rendered in real time and efficiently handles self occlusions, as well as the application of the Unscented Kalman filter to the problem of tracking articulated objects [39, 38]. 1.1 Structure of This Report The next chapter presents a brief literature survey of hand tracking and human body tracking. Chapter 3 reviews some of the material on projective geometry of quadrics and conics and the construction of the hand model. Details on filtering theory and a review of the ....

B. Stenger, P. R. S. Mendonca, and R. Cipolla. Model-based hand tracking using an unscented kalman filter. In Proc. British Machine Vision Conference, Manchester, UK, September 2001. to appear.

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B. Stenger, P. Mendonca, and R. Cipolla, "Model-- based hand tracking using an unscented kalman filter, " in British Machine Vision Conference, September 2001, vol. 1, pp. 63--72.

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Stenger, B., Mendona, P., Cipolla, R.: Model--based hand tracking using an unscented kalman filter. In: British Machine Vision Conference. Volume 1. (2001) 63--72

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B. Stenger, P. Mendonca, and R. Cipolla. Model-based hand tracking using an unscented kalman filter. In BMVC, page Session 2: Tracking and Sequences, 2001.

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Stenger, B., Mendona, P.S., Cipolla, R.: Model-based hand tracking using an unscented kalman filter. In: Proc. British Machine Vision Conference. Volume I., Manchester, UK (2001) 63--72

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B. Stenger, P. R. S. Mendonca, and R. Cipolla. Model-Based Hand Tracking Using an Unscented Kalman Filter. In Proceedings of British Machine Vision Conference, volume I, pages 63--72, Manchester, UK, September 2001.

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B. Stenger, P. R. S. Mendonca, and R. Cipolla, "Modelbased hand tracking using an unscented kalman filter," in Proc. British Machine Vision Conference, volume I, (Manchester, UK), September 2001, pp. 63--72.

No context found.

B. Stenger, P. R. S. Mendonca, and R. Cipolla. Model-based hand tracking using an unscented Kalman filter. In Proc. British Machine Vision Conference, volume I, pages 63--72, Manchester, UK, 2001.

No context found.

B. Stenger, P. R. S. Mendonca, and R. Cipolla, "Modelbased hand tracking using an unscented kalman filter," in Proc. British Machine Vision Conference, volume I, (Manchester, UK), September 2001, pp. 63--72.

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