Description: This experiment from the examples the opencv own sample, the example is to complete a one-dimensional tracking using kalman, ie, tracking a changing point of view. Performance in the interface as a point in the circle on steady running, then track the points. Looks like the tracking point is a two-dimensional, in fact, convert the angle is one-dimensional. The main application of the Kalman filter theory in the real world, not the ideal environment. Is the value of a variable to keep track of the basis for tracking the first according to the equation of motion of the system to the value forecast, for example, we know the velocity of an object, then the following moment of its location in accordance with the truth can be predicted , but the forecast is certainly a margin of error, only as a basis for tracking. Another is based on measurements to measure the value of that variable, Of course, this measurement error can only be used as the basis of these two basis weights the different pr
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kalman
......\img.jpg
......\kalman.cpp
......\kalman.h
......\kalman.pro
......\kalman.pro.user
......\kalman.ui
......\main.cpp
目标跟踪学习笔记_5(opencv中kalman点跟踪例子).pdf
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