minor typo corrections to python tutorials

doc/py_tutorials/py_video/py_bg_subtraction/py_bg_subtraction.markdown

@@ -20,7 +20,7 @@ extract the moving foreground from static background.
 If you have an image of background alone, like an image of the room without visitors, image of the road
 without vehicles etc, it is an easy job. Just subtract the new image from the background. You get
 the foreground objects alone. But in most of the cases, you may not have such an image, so we need
-to extract the background from whatever images we have. It become more complicated when there are
+to extract the background from whatever images we have. It becomes more complicated when there are
 shadows of the vehicles. Since shadows also move, simple subtraction will mark that also as
 foreground. It complicates things.
 
@@ -72,7 +72,7 @@ papers by Z.Zivkovic, "Improved adaptive Gaussian mixture model for background s
 and "Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction"
 in 2006. One important feature of this algorithm is that it selects the appropriate number of
 gaussian distribution for each pixel. (Remember, in last case, we took a K gaussian distributions
-throughout the algorithm). It provides better adaptibility to varying scenes due illumination
+throughout the algorithm). It provides better adaptability to varying scenes due illumination
 changes etc.
 
 As in previous case, we have to create a background subtractor object. Here, you have an option of

doc/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.markdown

@@ -75,10 +75,10 @@ solution.
 ( Check similarity of inverse matrix with Harris corner detector. It denotes that corners are better
 points to be tracked.)
 
-So from user point of view, idea is simple, we give some points to track, we receive the optical
+So from the user point of view, the idea is simple, we give some points to track, we receive the optical
 flow vectors of those points. But again there are some problems. Until now, we were dealing with
-small motions. So it fails when there is large motion. So again we go for pyramids. When we go up in
-the pyramid, small motions are removed and large motions becomes small motions. So applying
+small motions, so it fails when there is a large motion. To deal with this we use pyramids. When we go up in
+the pyramid, small motions are removed and large motions become small motions. So by applying
 Lucas-Kanade there, we get optical flow along with the scale.
 
 Lucas-Kanade Optical Flow in OpenCV