diff --git a/modules/video/include/opencv2/video/tracking.hpp b/modules/video/include/opencv2/video/tracking.hpp
index abd7f27b0cc844a2b56a6b7c9485e2aacd3cf8ea..fc36e2148a706cae7205852ff9b522ecfa97381f 100644
--- a/modules/video/include/opencv2/video/tracking.hpp
+++ b/modules/video/include/opencv2/video/tracking.hpp
@@ -280,9 +280,9 @@ public:
     //! the default constructor
     CV_WRAP KalmanFilter();
     //! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
-    CV_WRAP KalmanFilter(int dynamParams, int measureParams, int controlParams=0);
+    CV_WRAP KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
     //! re-initializes Kalman filter. The previous content is destroyed.
-    void init(int dynamParams, int measureParams, int controlParams=0);
+    void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
 
     //! computes predicted state
     CV_WRAP const Mat& predict(const Mat& control=Mat());
diff --git a/modules/video/src/kalman.cpp b/modules/video/src/kalman.cpp
index d34ffda3e0ff34720d5836172ef816ab6c458be7..4741f4965b49070af7c06403bbe96fe50cdfd257 100644
--- a/modules/video/src/kalman.cpp
+++ b/modules/video/src/kalman.cpp
@@ -211,38 +211,39 @@ namespace cv
 {
 
 KalmanFilter::KalmanFilter() {}
-KalmanFilter::KalmanFilter(int dynamParams, int measureParams, int controlParams)
+KalmanFilter::KalmanFilter(int dynamParams, int measureParams, int controlParams, int type)
 {
-    init(dynamParams, measureParams, controlParams);
+    init(dynamParams, measureParams, controlParams, type);
 }
 
-void KalmanFilter::init(int DP, int MP, int CP)
+void KalmanFilter::init(int DP, int MP, int CP, int type)
 {
     CV_Assert( DP > 0 && MP > 0 );
+    CV_Assert( type == CV_32F || type == CV_64F );
     CP = std::max(CP, 0);
 
-    statePre = Mat::zeros(DP, 1, CV_32F);
-    statePost = Mat::zeros(DP, 1, CV_32F);
-    transitionMatrix = Mat::eye(DP, DP, CV_32F);
+    statePre = Mat::zeros(DP, 1, type);
+    statePost = Mat::zeros(DP, 1, type);
+    transitionMatrix = Mat::eye(DP, DP, type);
 
-    processNoiseCov = Mat::eye(DP, DP, CV_32F);
-    measurementMatrix = Mat::zeros(MP, DP, CV_32F);
-    measurementNoiseCov = Mat::eye(MP, MP, CV_32F);
+    processNoiseCov = Mat::eye(DP, DP, type);
+    measurementMatrix = Mat::zeros(MP, DP, type);
+    measurementNoiseCov = Mat::eye(MP, MP, type);
 
-    errorCovPre = Mat::zeros(DP, DP, CV_32F);
-    errorCovPost = Mat::zeros(DP, DP, CV_32F);
-    gain = Mat::zeros(DP, MP, CV_32F);
+    errorCovPre = Mat::zeros(DP, DP, type);
+    errorCovPost = Mat::zeros(DP, DP, type);
+    gain = Mat::zeros(DP, MP, type);
 
     if( CP > 0 )
-        controlMatrix = Mat::zeros(DP, CP, CV_32F);
+        controlMatrix = Mat::zeros(DP, CP, type);
     else
         controlMatrix.release();
 
-    temp1.create(DP, DP, CV_32F);
-    temp2.create(MP, DP, CV_32F);
-    temp3.create(MP, MP, CV_32F);
-    temp4.create(MP, DP, CV_32F);
-    temp5.create(MP, 1, CV_32F);
+    temp1.create(DP, DP, type);
+    temp2.create(MP, DP, type);
+    temp3.create(MP, MP, type);
+    temp4.create(MP, DP, type);
+    temp5.create(MP, 1, type);
 }
 
 const Mat& KalmanFilter::predict(const Mat& control)