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diff --git a/python/openvino/runtime/common/demo_utils/src/kuhn_munkres.cpp b/python/openvino/runtime/common/demo_utils/src/kuhn_munkres.cpp
new file mode 100644
index 0000000..7d612c1
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+++ b/python/openvino/runtime/common/demo_utils/src/kuhn_munkres.cpp
@@ -0,0 +1,169 @@
+// Copyright (C) 2018-2019 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <algorithm>
+#include <limits>
+#include <vector>
+
+#include <utils/kuhn_munkres.hpp>
+
+KuhnMunkres::KuhnMunkres(bool greedy) : n_(), greedy_(greedy) {}
+
+std::vector<size_t> KuhnMunkres::Solve(const cv::Mat& dissimilarity_matrix) {
+    CV_Assert(dissimilarity_matrix.type() == CV_32F);
+    double min_val;
+    cv::minMaxLoc(dissimilarity_matrix, &min_val);
+
+    n_ = std::max(dissimilarity_matrix.rows, dissimilarity_matrix.cols);
+    dm_ = cv::Mat(n_, n_, CV_32F, cv::Scalar(0));
+    marked_ = cv::Mat(n_, n_, CV_8S, cv::Scalar(0));
+    points_ = std::vector<cv::Point>(n_ * 2);
+
+    dissimilarity_matrix.copyTo(dm_(
+            cv::Rect(0, 0, dissimilarity_matrix.cols, dissimilarity_matrix.rows)));
+
+    is_row_visited_ = std::vector<int>(n_, 0);
+    is_col_visited_ = std::vector<int>(n_, 0);
+
+    Run();
+
+    std::vector<size_t> results(dissimilarity_matrix.rows, -1);
+    for (int i = 0; i < dissimilarity_matrix.rows; i++) {
+        const auto ptr = marked_.ptr<char>(i);
+        for (int j = 0; j < dissimilarity_matrix.cols; j++) {
+            if (ptr[j] == kStar) {
+                results[i] = (size_t)j;
+            }
+        }
+    }
+    return results;
+}
+
+void KuhnMunkres::TrySimpleCase() {
+    auto is_row_visited = std::vector<int>(n_, 0);
+    auto is_col_visited = std::vector<int>(n_, 0);
+
+    for (int row = 0; row < n_; row++) {
+        auto ptr = dm_.ptr<float>(row);
+        auto marked_ptr = marked_.ptr<char>(row);
+        auto min_val = *std::min_element(ptr, ptr + n_);
+        for (int col = 0; col < n_; col++) {
+            ptr[col] -= min_val;
+            if (ptr[col] == 0 && !is_col_visited[col] && !is_row_visited[row]) {
+                marked_ptr[col] = kStar;
+                is_col_visited[col] = 1;
+                is_row_visited[row] = 1;
+            }
+        }
+    }
+}
+
+bool KuhnMunkres::CheckIfOptimumIsFound() {
+    int count = 0;
+    for (int i = 0; i < n_; i++) {
+        const auto marked_ptr = marked_.ptr<char>(i);
+        for (int j = 0; j < n_; j++) {
+            if (marked_ptr[j] == kStar) {
+                is_col_visited_[j] = 1;
+                count++;
+            }
+        }
+    }
+
+    return count >= n_;
+}
+
+cv::Point KuhnMunkres::FindUncoveredMinValPos() {
+    auto min_val = std::numeric_limits<float>::max();
+    cv::Point min_val_pos(-1, -1);
+    for (int i = 0; i < n_; i++) {
+        if (!is_row_visited_[i]) {
+            auto dm_ptr = dm_.ptr<float>(i);
+            for (int j = 0; j < n_; j++) {
+                if (!is_col_visited_[j] && dm_ptr[j] < min_val) {
+                    min_val = dm_ptr[j];
+                    min_val_pos = cv::Point(j, i);
+                }
+            }
+        }
+    }
+    return min_val_pos;
+}
+
+void KuhnMunkres::UpdateDissimilarityMatrix(float val) {
+    for (int i = 0; i < n_; i++) {
+        auto dm_ptr = dm_.ptr<float>(i);
+        for (int j = 0; j < n_; j++) {
+            if (is_row_visited_[i]) dm_ptr[j] += val;
+            if (!is_col_visited_[j]) dm_ptr[j] -= val;
+        }
+    }
+}
+
+int KuhnMunkres::FindInRow(int row, int what) {
+    for (int j = 0; j < n_; j++) {
+        if (marked_.at<char>(row, j) == what) {
+            return j;
+        }
+    }
+    return -1;
+}
+
+int KuhnMunkres::FindInCol(int col, int what) {
+    for (int i = 0; i < n_; i++) {
+        if (marked_.at<char>(i, col) == what) {
+            return i;
+        }
+    }
+    return -1;
+}
+
+void KuhnMunkres::Run() {
+    TrySimpleCase();
+    if (greedy_)
+        return;
+    while (!CheckIfOptimumIsFound()) {
+        while (true) {
+            auto point = FindUncoveredMinValPos();
+            auto min_val = dm_.at<float>(point.y, point.x);
+            if (min_val > 0) {
+                UpdateDissimilarityMatrix(min_val);
+            } else {
+                marked_.at<char>(point.y, point.x) = kPrime;
+                int col = FindInRow(point.y, kStar);
+                if (col >= 0) {
+                    is_row_visited_[point.y] = 1;
+                    is_col_visited_[col] = 0;
+                } else {
+                    int count = 0;
+                    points_[count] = point;
+
+                    while (true) {
+                        int row = FindInCol(points_[count].x, kStar);
+                        if (row >= 0) {
+                            count++;
+                            points_[count] = cv::Point(points_[count - 1].x, row);
+                            int col = FindInRow(points_[count].y, kPrime);
+                            count++;
+                            points_[count] = cv::Point(col, points_[count - 1].y);
+                        } else {
+                            break;
+                        }
+                    }
+
+                    for (int i = 0; i < count + 1; i++) {
+                        auto& mark = marked_.at<char>(points_[i].y, points_[i].x);
+                        mark = mark == kStar ? 0 : kStar;
+                    }
+
+                    is_row_visited_ = std::vector<int>(n_, 0);
+                    is_col_visited_ = std::vector<int>(n_, 0);
+
+                    marked_.setTo(0, marked_ == kPrime);
+                    break;
+                }
+            }
+        }
+    }
+}