2009. 10. 27. 23:31
Computer Vision
R. L. Thompson, I. D. Reid, L. A. Munoz, and D. W. Murray,
“Providing synthetic views for teleoperation using visual pose tracking in multiple cameras,”
IEEE Transactions on Systems, Man and Cybernetics, Part A, vol. 31, no. 1, pp. 43–54, 2001.
Abstract - This paper describes a visual tool for teleoperative experimentation involving remote manipulation and contact tasks. Using modest hardware, it recovers in real-time the pose of moving polyhedral objects, and presents a synthetic view of the scene to the teleoperator using any chosen viewpoint and viewing direction. The method of line tracking introduced by Harris is extended to multiple calibrated cameras, and afforced by robust methods and iterative ltering. Experiments are reported which determine the static and dynamic performance of the vision system, and its use in teleoperation is illustrated in two experiments, a peg in hole manipulation task and an impact control task.
Line tracking
http://en.wikipedia.org/wiki/Passive_radar#Line_tracking
The line-tracking step refers to the tracking of target returns from individual targets, over time, in the range-Doppler space produced by the cross-correlation processing. A standard Kalman filter is typically used. Most false alarms are rejected during this stage of the processing.
- Three difficulties using the Harris tracker
First it was found to be easily broken by occlusions and changing lighting. Robust methods to mitigate this problem have been investigated monocularly by Armstrong and Zisserman [20], [21]. Although this has a marked effect on tracking performance, the second problem found is that the accuracy of the pose recovered in a single camera was poor, with evident correlation between depth and rotation about axes parallel to the image plane. Maitland and Harris [22] had already noted as much when recovering the pose of a pointing device destined for neurosurgical application [23].
They reported much improved accuracy using two cameras; but the object was stationary, had an elaborate pattern drawn on it and was visible at all times to both cameras. The third difficulty, or rather uncertainty, was that the convergence properties and dynamic performances of the monocular and multicamera methods were largely unreported.
"Harris' RAPiD tracker included a constant velocity Kalman filter."
“Providing synthetic views for teleoperation using visual pose tracking in multiple cameras,”
IEEE Transactions on Systems, Man and Cybernetics, Part A, vol. 31, no. 1, pp. 43–54, 2001.
thompson_etal_tsmc2001.pdfAbstract - This paper describes a visual tool for teleoperative experimentation involving remote manipulation and contact tasks. Using modest hardware, it recovers in real-time the pose of moving polyhedral objects, and presents a synthetic view of the scene to the teleoperator using any chosen viewpoint and viewing direction. The method of line tracking introduced by Harris is extended to multiple calibrated cameras, and afforced by robust methods and iterative ltering. Experiments are reported which determine the static and dynamic performance of the vision system, and its use in teleoperation is illustrated in two experiments, a peg in hole manipulation task and an impact control task.
Line tracking
http://en.wikipedia.org/wiki/Passive_radar#Line_tracking
The line-tracking step refers to the tracking of target returns from individual targets, over time, in the range-Doppler space produced by the cross-correlation processing. A standard Kalman filter is typically used. Most false alarms are rejected during this stage of the processing.
- Three difficulties using the Harris tracker
First it was found to be easily broken by occlusions and changing lighting. Robust methods to mitigate this problem have been investigated monocularly by Armstrong and Zisserman [20], [21]. Although this has a marked effect on tracking performance, the second problem found is that the accuracy of the pose recovered in a single camera was poor, with evident correlation between depth and rotation about axes parallel to the image plane. Maitland and Harris [22] had already noted as much when recovering the pose of a pointing device destined for neurosurgical application [23].
They reported much improved accuracy using two cameras; but the object was stationary, had an elaborate pattern drawn on it and was visible at all times to both cameras. The third difficulty, or rather uncertainty, was that the convergence properties and dynamic performances of the monocular and multicamera methods were largely unreported.
"Harris' RAPiD tracker included a constant velocity Kalman filter."
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