diff --git a/modules/video/include/opencv2/video/tracking.hpp b/modules/video/include/opencv2/video/tracking.hpp
index c6ead3a42c..a96a2e53cf 100644
--- a/modules/video/include/opencv2/video/tracking.hpp
+++ b/modules/video/include/opencv2/video/tracking.hpp
@@ -297,7 +297,7 @@ row is ignored.
 Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
 area-based alignment that builds on intensity similarities. In essence, the function updates the
 initial transformation that roughly aligns the images. If this information is missing, the identity
-warp (unity matrix) should be given as input. Note that if images undergo strong
+warp (unity matrix) is used as an initialization. Note that if images undergo strong
 displacements/rotations, an initial transformation that roughly aligns the images is necessary
 (e.g., a simple euclidean/similarity transform that allows for the images showing the same image
 content approximately). Use inverse warping in the second image to take an image close to the first
diff --git a/modules/video/src/ecc.cpp b/modules/video/src/ecc.cpp
index d11419e402..377b775072 100644
--- a/modules/video/src/ecc.cpp
+++ b/modules/video/src/ecc.cpp
@@ -325,6 +325,16 @@ double cv::findTransformECC(InputArray templateImage,
     CV_Assert(!src.empty());
     CV_Assert(!dst.empty());
 
+    // If the user passed an un-initialized warpMatrix, initialize to identity
+    if(map.empty()) {
+        int rowCount = 2;
+        if(motionType == MOTION_HOMOGRAPHY)
+            rowCount = 3;
+
+        warpMatrix.create(rowCount, 3, CV_32FC1);
+        map = warpMatrix.getMat();
+        map = Mat::eye(rowCount, 3, CV_32F);
+    }
 
     if( ! (src.type()==dst.type()))
         CV_Error( Error::StsUnmatchedFormats, "Both input images must have the same data type" );