diff --git a/modules/video/doc/motion_analysis_and_object_tracking.rst b/modules/video/doc/motion_analysis_and_object_tracking.rst
index 12783fce6..70ba2b9d6 100644
--- a/modules/video/doc/motion_analysis_and_object_tracking.rst
+++ b/modules/video/doc/motion_analysis_and_object_tracking.rst
@@ -8,9 +8,9 @@ calcOpticalFlowPyrLK
 ------------------------
 Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
 
-.. ocv:function:: void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err, Size winSize=Size(15,15), int maxLevel=3,        TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), double derivLambda=0.5, int flags=0 )
+.. ocv:function:: void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err, Size winSize=Size(15,15), int maxLevel=3, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThreshold=1e-4)
 
-.. ocv:pyfunction:: cv2.calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, derivLambda[, flags]]]]]]]]) -> nextPts, status, err
+.. ocv:pyfunction:: cv2.calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, flags[, minEigThreshold]]]]]]]]) -> nextPts, status, err
 
 .. ocv:cfunction:: void cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr, CvArr* prevPyr, CvArr* currPyr, const CvPoint2D32f* prevFeatures, CvPoint2D32f* currFeatures, int count, CvSize winSize, int level, char* status, float* trackError, CvTermCriteria criteria, int flags )
 .. ocv:pyoldfunction:: cv.CalcOpticalFlowPyrLK( prev, curr, prevPyr, currPyr, prevFeatures, winSize, level, criteria, flags, guesses=None) -> (currFeatures, status, trackError)
@@ -25,7 +25,7 @@ Calculates an optical flow for a sparse feature set using the iterative Lucas-Ka
 
     :param status: Output status vector. Each element of the vector is set to 1 if the flow for the corresponding features has been found. Otherwise, it is set to 0.
 
-    :param err: Output vector that contains the difference between patches around the original and moved points.
+    :param err: Output vector of errors. Each element of the vector is set to a error for the corresponding feature. A type of the error measure can be set in ``flags`` parameter. If the flow wasn't found then the error is not defined (use the ``status`` parameter to find such cases).
 
     :param winSize: Size of the search window at each pyramid level.
 
@@ -33,11 +33,13 @@ Calculates an optical flow for a sparse feature set using the iterative Lucas-Ka
 
     :param criteria: Parameter specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations  ``criteria.maxCount``  or when the search window moves by less than  ``criteria.epsilon`` .
     
-    :param derivLambda: Not used.
-
     :param flags: Operation flags:
 
             * **OPTFLOW_USE_INITIAL_FLOW** Use initial estimations stored in  ``nextPts`` . If the flag is not set, then ``prevPts`` is copied to ``nextPts`` and is considered as the initial estimate.
+
+            * **OPTFLOW_LK_GET_MIN_EIGENVALS** Use minimum eigen values as a error measure (see ``minEigThreshold`` description). If the flag is not set, then L1 norm between patches around the original and a moved point divided by number of pixels in a window is used as a error measure.
+
+    :param minEigThreshold: The algorithm computes a minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]_) divided by number of pixels in a window. If this value is less then ``minEigThreshold`` then a corresponding feature is filtered out and its flow is not computed. So it allows to remove bad points earlier and speed up the computation.
             
 The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
 [Bouguet00]_.