Finds an object on a back projection image

window = cv.meanShift(probImage, window)
[window,iter] = cv.meanShift(probImage, window)
[...] = cv.meanShift(..., 'OptionName', optionValue, ...)

Input

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

Output

• window Converged CAMSHIFT window [x,y,w,h].
• iter Number of iterations CAMSHIFT took to converge.

Options

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

The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in window of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations Criteria.maxCount is done or until the window center shifts by less than Criteria.epsilon. The algorithm is used inside cv.CamShift and, unlike cv.CamShift, the search window size or orientation do not change during the search. You can simply pass the output of cv.calcBackProject to this function. But better results can be obtained if you pre-filter the back projection and remove the noise. For example, you can do this by retrieving connected components with cv.findContours, throwing away contours with small area (cv.contourArea), and rendering the remaining contours with cv.drawContours.