Finds an object center, size, and orientation

box = cv.CamShift(probImage, window)
[box,window] = cv.CamShift(probImage, window)
[...] = cv.CamShift(..., 'OptionName', optionValue, ...)

Input

• probImage Back projection of the object histogram. See cv.calcBackProject for details.
• window Initial search window [x,y,w,h].

Output

• box Output rectangle with rotation. A scalar structure of the form: struct('center',[x,y], 'size',[w,h], 'angle',a)
• window Converged CamShift window [x,y,w,h]

Options

• Criteria Stop criteria for the underlying cv.meanShift. Accepts a struct with 'type', 'maxCount', and 'epsilon' fields. Default struct('type','Count+EPS', 'maxCount',100, 'epsilon',1.0)

The function implements the CamShift object tracking algorithm [Bradski98]. First, it finds an object center using cv.meanShift and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with cv.RotatedRect.boundingRect.

References

[Bradski98]:

Gary R Bradski. Computer vision face tracking for use in a perceptual user interface. 1998.