Robust target tracking method based on support vector machine
A support vector machine and support vector technology, applied in image data processing, instrumentation, computing and other directions, can solve problems such as tracking drift, achieve good real-time and robustness, and solve the effect of tracking drift
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[0026] 1, figure 1 Shown is the method of extracting samples in the update support vector stage. The center point is the center of the current positive sample frame. The sample network group is constructed according to the method of bisecting the radius and angle, and the points are staggered on the radius. This method of taking points can be used in Reduce the amount of computation without reducing the number of features.
[0027] 2, figure 2 Shown are the two parts of the Kalman filter, the time update and the measurement update, where, Indicates the position of the target in k frames, u k-1 Indicates the control amount of k-1 frames to the system, this paper sets u k-1 to zero, P k is the error covariance, Q and R are process noise and measurement noise respectively, which do not change with system state changes. A is the state transition matrix: A = 1 ...
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