diff --git a/modules/xobjdetect/include/opencv2/xobjdetect/private.hpp b/modules/xobjdetect/include/opencv2/xobjdetect/private.hpp
new file mode 100644
index 000000000..4201263cc
--- /dev/null
+++ b/modules/xobjdetect/include/opencv2/xobjdetect/private.hpp
@@ -0,0 +1,62 @@
+#ifndef __OPENCV_XOBJDETECT_PRIVATE_HPP__
+#define __OPENCV_XOBJDETECT_PRIVATE_HPP__
+
+#ifndef __OPENCV_BUILD
+#  error this is a private header, do not include it outside OpenCV
+#endif
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+namespace xobjdetect
+{
+
+class CV_EXPORTS Stump
+{
+public:
+
+    /* Initialize zero stump */
+    Stump(): threshold_(0), polarity_(1), pos_value_(1), neg_value_(-1) {}
+
+    /* Initialize stump with given threshold, polarity
+        and classification values */
+    Stump(int threshold, int polarity, float pos_value, float neg_value):
+        threshold_(threshold), polarity_(polarity),
+        pos_value_(pos_value), neg_value_(neg_value) {}
+
+    /* Train stump for given data
+
+        data — matrix of feature values, size M x N, one feature per row
+
+        labels — matrix of sample class labels, size 1 x N. Labels can be from
+            {-1, +1}
+
+        weights — matrix of sample weights, size 1 x N
+
+    Returns chosen feature index. Feature enumeration starts from 0
+    */
+    int train(const Mat& data, const Mat& labels, const Mat& weights);
+
+    /* Predict object class given
+
+        value — feature value. Feature must be the same as was chosen
+        during training stump
+
+    Returns real value, sign(value) means class
+    */
+    float predict(int value) const;
+
+private:
+    /* Stump decision threshold */
+    int threshold_;
+    /* Stump polarity, can be from {-1, +1} */
+    int polarity_;
+    /* Classification values for positive and negative classes  */
+    float pos_value_, neg_value_;
+};
+
+} /* namespace xobjdetect */
+} /* namespace cv */
+
+#endif // __OPENCV_XOBJDETECT_PRIVATE_HPP__