diff --git a/modules/tracking/src/multiTracker.cpp b/modules/tracking/src/multiTracker.cpp
index d7bb283e6..78f0ee125 100644
--- a/modules/tracking/src/multiTracker.cpp
+++ b/modules/tracking/src/multiTracker.cpp
@@ -75,7 +75,6 @@ namespace cv
 
 	bool MultiTracker::update(const Mat& image)
 	{
-		printf("Naive-Loop MO-TLD Update....\n");
 		for (int i = 0; i < (int)trackers.size(); i++)
 			if (!trackers[i]->update(image, boundingBoxes[i]))
 				return false;
@@ -87,8 +86,6 @@ namespace cv
 	/*Optimized update method for TLD Multitracker */
 	bool MultiTrackerTLD::update_opt(const Mat& image)
 	{
-		printf("Optimized MO-TLD Update....\n");
-
 		//Get parameters from first object
 		//TLD Tracker data extraction
 		Tracker* trackerPtr = trackers[0];
@@ -186,16 +183,7 @@ namespace cv
 
 #if 1
 			if (it != candidatesRes[k].end())
-			{
 				tld::resample(imageForDetector, candidates[k][it - candidatesRes[k].begin()], standardPatch);
-				//dfprintf((stderr, "%d %f %f\n", data->frameNum, tldModel->Sc(standardPatch), tldModel->Sr(standardPatch)));
-				//if( candidatesRes.size() == 2 &&  it == (candidatesRes.begin() + 1) )
-				//dfprintf((stderr, "detector WON\n"));
-			}
-			else
-			{
-				//dfprintf((stderr, "%d x x\n", data->frameNum));
-			}
 #endif
 
 			if (*it > tld::CORE_THRESHOLD)
@@ -226,7 +214,6 @@ namespace cv
 					detectorResults[k][i].isObject = expertResult;
 				}
 				tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults[k]);
-				//dprintf(("%d relabeled by nExpert\n", negRelabeled));
 				pExpert.additionalExamples(examplesForModel, examplesForEnsemble);
 				if (ocl::haveOpenCL())
 					tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, true);
@@ -249,14 +236,7 @@ namespace cv
 
 
 		}
-		//Debug display candidates after Variance Filter
-		////////////////////////////////////////////////
-		Mat tmpImg = image;
-		for (int i = 0; i < (int)debugStack[0].size(); i++)
-			//rectangle(tmpImg, debugStack[0][i], Scalar(255, 255, 255), 1, 1, 0);
-		debugStack[0].clear();
-		tmpImg.copyTo(image);
-		////////////////////////////////////////////////
+
 		return true;
 	}
 
@@ -290,10 +270,6 @@ namespace cv
 		std::vector <Point> tmpP;
 		std::vector <int> tmpI;
 
-		//int64 e1, e2;
-		//double t;
-		//e1 = getTickCount();
-
 		//Detection part
 		//Generate windows and filter by variance
 		scaleID = 0;
@@ -344,10 +320,6 @@ namespace cv
 							continue;
 						varBuffer[k].push_back(Point(dx * i, dy * j));
 						varScaleIDs[k].push_back(scaleID);
-
-						//Debug display candidates after Variance Filter
-						double curScale = pow(tld::SCALE_STEP, scaleID);
-						debugStack[0].push_back(Rect2d(dx * i* curScale, dy * j*curScale, tldModel->getMinSize().width*curScale, tldModel->getMinSize().height*curScale));
 					}
 				}
 			}
@@ -361,16 +333,7 @@ namespace cv
 			blurred_imgs.push_back(tmp);
 		} while (size.width >= initSize.width && size.height >= initSize.height);
 
-
-
-		//e2 = getTickCount();
-		//t = (e2 - e1) / getTickFrequency()*1000.0;
-		//printf("Variance: %d\t%f\n", varBuffer.size(), t);
-
-		//printf("OrigVar 1: %f\n", *tldModel->detector->originalVariancePtr);
-
 		//Encsemble classification
-		//e1 = getTickCount();
 		for (int k = 0; k < (int)trackers.size(); k++)
 		{
 			//TLD Tracker data extraction
@@ -410,29 +373,9 @@ namespace cv
 				ensBuffer[k].push_back(varBuffer[k][i]);
 				ensScaleIDs[k].push_back(varScaleIDs[k][i]);
 			}
-			/*
-			for (int i = 0; i < (int)varBuffer[k].size(); i++)
-			{
-			tldModel->detector->prepareClassifiers(static_cast<int> (blurred_imgs[varScaleIDs[k][i]].step[0]));
-			if (tldModel->detector->ensembleClassifierNum(&blurred_imgs[varScaleIDs[k][i]].at<uchar>(varBuffer[k][i].y, varBuffer[k][i].x)) <= tld::ENSEMBLE_THRESHOLD)
-			continue;
-			ensBuffer[k].push_back(varBuffer[k][i]);
-			ensScaleIDs[k].push_back(varScaleIDs[k][i]);
-			}
-			*/
 		}
-		//e2 = getTickCount();
-		//t = (e2 - e1) / getTickFrequency()*1000.0;
-		//printf("Ensemble: %d\t%f\n", ensBuffer.size(), t);
-
-		//printf("varBuffer 1: %d\n", varBuffer[0].size());
-		//printf("ensBuffer 1: %d\n", ensBuffer[0].size());
-
-		//printf("varBuffer 2: %d\n", varBuffer[1].size());
-		//printf("ensBuffer 2: %d\n", ensBuffer[1].size());
 
 		//NN classification
-		//e1 = getTickCount();
 		for (int k = 0; k < (int)trackers.size(); k++)
 		{
 			//TLD Tracker data extraction
@@ -477,7 +420,6 @@ namespace cv
 					maxSc = scValue;
 					maxScRect = labPatch.rect;
 				}
-				//printf("%d	%f	%f\n", k, srValue, scValue);
 			}
 
 
@@ -487,13 +429,9 @@ namespace cv
 			else
 			{
 				res[k] = maxScRect;
-				//printf("%f %f %f %f\n", maxScRect.x, maxScRect.y, maxScRect.width, maxScRect.height);
 				detect_flgs[k] = true;
 			}
 		}
-		//e2 = getTickCount();
-		//t = (e2 - e1) / getTickFrequency()*1000.0;
-		//printf("NN: %d\t%f\n", patches.size(), t);
 	}
 
 	void ocl_detect_all(const Mat& img, const Mat& imgBlurred, std::vector<Rect2d>& res, std::vector < std::vector < tld::TLDDetector::LabeledPatch > > &patches, std::vector<bool> &detect_flgs,
@@ -526,10 +464,6 @@ namespace cv
 		std::vector <Point> tmpP;
 		std::vector <int> tmpI;
 
-		//int64 e1, e2;
-		//double t;
-		//e1 = getTickCount();
-
 		//Detection part
 		//Generate windows and filter by variance
 		scaleID = 0;
@@ -580,10 +514,6 @@ namespace cv
 							continue;
 						varBuffer[k].push_back(Point(dx * i, dy * j));
 						varScaleIDs[k].push_back(scaleID);
-
-						//Debug display candidates after Variance Filter
-						double curScale = pow(tld::SCALE_STEP, scaleID);
-						debugStack[0].push_back(Rect2d(dx * i* curScale, dy * j*curScale, tldModel->getMinSize().width*curScale, tldModel->getMinSize().height*curScale));
 					}
 				}
 			}
@@ -597,16 +527,7 @@ namespace cv
 			blurred_imgs.push_back(tmp);
 		} while (size.width >= initSize.width && size.height >= initSize.height);
 
-
-
-		//e2 = getTickCount();
-		//t = (e2 - e1) / getTickFrequency()*1000.0;
-		//printf("Variance: %d\t%f\n", varBuffer.size(), t);
-
-		//printf("OrigVar 1: %f\n", *tldModel->detector->originalVariancePtr);
-
 		//Encsemble classification
-		//e1 = getTickCount();
 		for (int k = 0; k < (int)trackers.size(); k++)
 		{
 			//TLD Tracker data extraction
@@ -646,28 +567,9 @@ namespace cv
 				ensBuffer[k].push_back(varBuffer[k][i]);
 				ensScaleIDs[k].push_back(varScaleIDs[k][i]);
 			}
-			/*
-			for (int i = 0; i < (int)varBuffer[k].size(); i++)
-			{
-			tldModel->detector->prepareClassifiers(static_cast<int> (blurred_imgs[varScaleIDs[k][i]].step[0]));
-			if (tldModel->detector->ensembleClassifierNum(&blurred_imgs[varScaleIDs[k][i]].at<uchar>(varBuffer[k][i].y, varBuffer[k][i].x)) <= tld::ENSEMBLE_THRESHOLD)
-			continue;
-			ensBuffer[k].push_back(varBuffer[k][i]);
-			ensScaleIDs[k].push_back(varScaleIDs[k][i]);
-			}
-			*/
 		}
-		//e2 = getTickCount();
-		//t = (e2 - e1) / getTickFrequency()*1000.0;
-
-		//printf("varBuffer 1: %d\n", varBuffer[0].size());
-		//printf("ensBuffer 1: %d\n", ensBuffer[0].size());
-
-		//printf("varBuffer 2: %d\n", varBuffer[1].size());
-		//printf("ensBuffer 2: %d\n", ensBuffer[1].size());
 
 		//NN classification
-		//e1 = getTickCount();
 		for (int k = 0; k < (int)trackers.size(); k++)
 		{
 			//TLD Tracker data extraction
@@ -675,7 +577,6 @@ namespace cv
 			tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
 			//TLD Model Extraction
 			tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
-			//Size InitSize = tldModel->getMinSize();
 			npos = 0;
 			nneg = 0;
 			maxSc = -5.0;
@@ -730,7 +631,6 @@ namespace cv
 					maxSc = scValue;
 					maxScRect = labPatch.rect;
 				}
-				//printf("%d	%f	%f\n", k, srValue, scValue);
 			}
 
 
@@ -740,13 +640,9 @@ namespace cv
 			else
 			{
 				res[k] = maxScRect;
-				//printf("%f %f %f %f\n", maxScRect.x, maxScRect.y, maxScRect.width, maxScRect.height);
 				detect_flgs[k] = true;
 			}
 		}
-		//e2 = getTickCount();
-		//t = (e2 - e1) / getTickFrequency()*1000.0;
-		//printf("NN: %d\t%f\n", patches.size(), t);
 	}
 
 }
\ No newline at end of file
diff --git a/modules/tracking/src/multiTracker.hpp b/modules/tracking/src/multiTracker.hpp
index c98d7df66..9d363802c 100644
--- a/modules/tracking/src/multiTracker.hpp
+++ b/modules/tracking/src/multiTracker.hpp
@@ -53,6 +53,5 @@ namespace cv
 		std::vector<bool>& detect_flgs,	std::vector<Ptr<Tracker> >& trackers);
 	void ocl_detect_all(const Mat& img, const Mat& imgBlurred, std::vector<Rect2d>& res, std::vector < std::vector < tld::TLDDetector::LabeledPatch > > &patches,
 		std::vector<bool>& detect_flgs, std::vector<Ptr<Tracker> >& trackers);
-	std::vector <Rect2d> debugStack[10];
 }
 #endif
\ No newline at end of file
diff --git a/modules/tracking/src/tldDetector.cpp b/modules/tracking/src/tldDetector.cpp
index bfef69a45..ba9f03d9f 100644
--- a/modules/tracking/src/tldDetector.cpp
+++ b/modules/tracking/src/tldDetector.cpp
@@ -65,25 +65,6 @@ namespace cv
 		// Calculate Relative similarity of the patch (NN-Model)
 		double TLDDetector::Sr(const Mat_<uchar>& patch)
 		{
-			/*
-			int64 e1, e2;
-			float t;
-			e1 = getTickCount();
-			double splus = 0.0, sminus = 0.0;
-			for (int i = 0; i < (int)(*positiveExamples).size(); i++)
-				splus = std::max(splus, 0.5 * (NCC((*positiveExamples)[i], patch) + 1.0));
-			for (int i = 0; i < (int)(*negativeExamples).size(); i++)
-				sminus = std::max(sminus, 0.5 * (NCC((*negativeExamples)[i], patch) + 1.0));
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
-			printf("Sr: %f\n", t);
-			if (splus + sminus == 0.0)
-				return 0.0;
-			return splus / (sminus + splus);
-			*/
-			//int64 e1, e2;
-			//float t;
-			//e1 = getTickCount();
 			double splus = 0.0, sminus = 0.0;
 			Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
 			for (int i = 0; i < *posNum; i++)
@@ -96,9 +77,7 @@ namespace cv
 				modelSample.data = &(negExp->data[i * 225]);
 				sminus = std::max(sminus, 0.5 * (NCC(modelSample, patch) + 1.0));
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Sr CPU: %f\n", t);
+
 			if (splus + sminus == 0.0)
 				return 0.0;
 			return splus / (sminus + splus);
@@ -106,10 +85,6 @@ namespace cv
 
 		double TLDDetector::ocl_Sr(const Mat_<uchar>& patch)
 		{
-			//int64 e1, e2, e3, e4;
-			//double t;
-			//e1 = getTickCount();
-			//e3 = getTickCount();
 			double splus = 0.0, sminus = 0.0;
 
 
@@ -134,38 +109,12 @@ namespace cv
 				*posNum,
 				*negNum);
 
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Mem Cpy GPU: %f\n", t);
-
 			size_t globSize = 1000;
-			//e3 = getTickCount();
+
 			if (!k.run(1, &globSize, NULL, false))
 				printf("Kernel Run Error!!!");
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Kernel Run GPU: %f\n", t);
 
-			//e3 = getTickCount();
 			Mat resNCC = devNCC.getMat(ACCESS_READ);
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Read Mem GPU: %f\n", t);
-
-			////Compare
-			//Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
-			//for (int i = 0; i < 200; i+=17)
-			//{
-			//	modelSample.data = &(posExp->data[i * 225]);
-			//	printf("%f\t%f\n\n", resNCC.at<float>(i), NCC(modelSample, patch));
-			//}
-
-			//for (int i = 0; i < 200; i+=23)
-			//{
-			//	modelSample.data = &(negExp->data[i * 225]);
-			//	printf("%f\t%f\n", resNCC.at<float>(500+i), NCC(modelSample, patch));
-			//}
-
 
 			for (int i = 0; i < *posNum; i++)
 				splus = std::max(splus, 0.5 * (resNCC.at<float>(i) + 1.0));
@@ -173,10 +122,6 @@ namespace cv
 			for (int i = 0; i < *negNum; i++)
 				sminus = std::max(sminus, 0.5 * (resNCC.at<float>(i+500) +1.0));
 
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Sr GPU: %f\n\n", t);
-
 			if (splus + sminus == 0.0)
 				return 0.0;
 			return splus / (sminus + splus);
@@ -184,11 +129,6 @@ namespace cv
 
 		void TLDDetector::ocl_batchSrSc(const Mat_<uchar>& patches, double *resultSr, double *resultSc, int numOfPatches)
 		{
-			//int64 e1, e2, e3, e4;
-			//double t;
-			//e1 = getTickCount();
-			//e3 = getTickCount();
-
 			UMat devPatches = patches.getUMat(ACCESS_READ, USAGE_ALLOCATE_DEVICE_MEMORY);
 			UMat devPositiveSamples = posExp->getUMat(ACCESS_READ, USAGE_ALLOCATE_DEVICE_MEMORY);
 			UMat devNegativeSamples = negExp->getUMat(ACCESS_READ, USAGE_ALLOCATE_DEVICE_MEMORY);
@@ -212,25 +152,13 @@ namespace cv
 				*negNum,
 				numOfPatches);
 
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Mem Cpy GPU: %f\n", t);
-
-			// 2 -> Pos&Neg
 			size_t globSize = 2 * numOfPatches*MAX_EXAMPLES_IN_MODEL;
-			//e3 = getTickCount();
+
 			if (!k.run(1, &globSize, NULL, true))
 				printf("Kernel Run Error!!!");
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Kernel Run GPU: %f\n", t);
 
-			//e3 = getTickCount();
 			Mat posNCC = devPosNCC.getMat(ACCESS_READ);
 			Mat negNCC = devNegNCC.getMat(ACCESS_READ);
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Read Mem GPU: %f\n", t);
 
 			//Calculate Srs
 			for (int id = 0; id < numOfPatches; id++)
@@ -256,62 +184,11 @@ namespace cv
 				else
 					resultSc[id] = spc / (smc + spc);
 			}
-
-			////Compare positive NCCs
-			/*Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
-			Mat_<uchar> patch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
-			for (int j = 0; j < numOfPatches; j++)
-			{
-				for (int i = 0; i < 1; i++)
-				{
-					modelSample.data = &(posExp->data[i * 225]);
-					patch.data = &(patches.data[j * 225]);
-					printf("%f\t%f\n", resultSr[j], Sr(patch));
-					printf("%f\t%f\n", resultSc[j], Sc(patch));
-				}
-			}*/
-
-			//for (int i = 0; i < 200; i+=23)
-			//{
-			//	modelSample.data = &(negExp->data[i * 225]);
-			//	printf("%f\t%f\n", resNCC.at<float>(500+i), NCC(modelSample, patch));
-			//}
-
-
-
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Sr GPU: %f\n\n", t);
 		}
 
 		// Calculate Conservative similarity of the patch (NN-Model)
 		double TLDDetector::Sc(const Mat_<uchar>& patch)
 		{
-			/*
-			int64 e1, e2;
-			float t;
-			e1 = getTickCount();
-			double splus = 0.0, sminus = 0.0;
-			int med = getMedian((*timeStampsPositive));
-			for (int i = 0; i < (int)(*positiveExamples).size(); i++)
-			{
-				if ((int)(*timeStampsPositive)[i] <= med)
-					splus = std::max(splus, 0.5 * (NCC((*positiveExamples)[i], patch) + 1.0));
-			}
-			for (int i = 0; i < (int)(*negativeExamples).size(); i++)
-				sminus = std::max(sminus, 0.5 * (NCC((*negativeExamples)[i], patch) + 1.0));
-			e2 = getTickCount();
-			t = (e2 - e1) / getTickFrequency()*1000.0;
-			printf("Sc: %f\n", t);
-			if (splus + sminus == 0.0)
-				return 0.0;
-
-			return splus / (sminus + splus);
-			*/
-
-			//int64 e1, e2;
-			//double t;
-			//e1 = getTickCount();
 			double splus = 0.0, sminus = 0.0;
 			Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
 			int med = getMedian((*timeStampsPositive));
@@ -328,9 +205,7 @@ namespace cv
 				modelSample.data = &(negExp->data[i * 225]);
 				sminus = std::max(sminus, 0.5 * (NCC(modelSample, patch) + 1.0));
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Sc: %f\n", t);
+
 			if (splus + sminus == 0.0)
 				return 0.0;
 
@@ -339,13 +214,8 @@ namespace cv
 
 		double TLDDetector::ocl_Sc(const Mat_<uchar>& patch)
 		{
-			//int64 e1, e2, e3, e4;
-			//float t;
-			//e1 = getTickCount();
 			double splus = 0.0, sminus = 0.0;
 
-			//e3 = getTickCount();
-
 			UMat devPatch = patch.getUMat(ACCESS_READ, USAGE_ALLOCATE_DEVICE_MEMORY);
 			UMat devPositiveSamples = posExp->getUMat(ACCESS_READ, USAGE_ALLOCATE_DEVICE_MEMORY);
 			UMat devNegativeSamples = negExp->getUMat(ACCESS_READ, USAGE_ALLOCATE_DEVICE_MEMORY);
@@ -367,37 +237,12 @@ namespace cv
 				*posNum,
 				*negNum);
 
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Mem Cpy GPU: %f\n", t);
-
 			size_t globSize = 1000;
-			//e3 = getTickCount();
+
 			if (!k.run(1, &globSize, NULL, false))
 				printf("Kernel Run Error!!!");
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Kernel Run GPU: %f\n", t);
 
-			//e3 = getTickCount();
 			Mat resNCC = devNCC.getMat(ACCESS_READ);
-			//e4 = getTickCount();
-			//t = (e4 - e3) / getTickFrequency()*1000.0;
-			//printf("Read Mem GPU: %f\n", t);
-
-			////Compare
-			//Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
-			//for (int i = 0; i < 200; i+=17)
-			//{
-			//	modelSample.data = &(posExp->data[i * 225]);
-			//	printf("%f\t%f\n\n", resNCC.at<float>(i), NCC(modelSample, patch));
-			//}
-
-			//for (int i = 0; i < 200; i+=23)
-			//{
-			//	modelSample.data = &(negExp->data[i * 225]);
-			//	printf("%f\t%f\n", resNCC.at<float>(500+i), NCC(modelSample, patch));
-			//}
 
 			int med = getMedian((*timeStampsPositive));
 			for (int i = 0; i < *posNum; i++)
@@ -407,10 +252,6 @@ namespace cv
 			for (int i = 0; i < *negNum; i++)
 				sminus = std::max(sminus, 0.5 * (resNCC.at<float>(i + 500) + 1.0));
 
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Sc GPU: %f\n\n", t);
-
 			if (splus + sminus == 0.0)
 				return 0.0;
 			return splus / (sminus + splus);
@@ -449,7 +290,6 @@ namespace cv
 					break;
 				}
 			}
-			//dprintf(("%d rects in res\n", (int)res.size()));
 		}
 
 		//Detection - returns most probable new target location (Max Sc)
@@ -469,10 +309,6 @@ namespace cv
 			std::vector <Mat> resized_imgs, blurred_imgs;
 			std::vector <Point> varBuffer, ensBuffer;
 			std::vector <int> varScaleIDs, ensScaleIDs;
-			//int64 e1, e2;
-			//double t;
-
-			//e1 = getTickCount();
 
 			//Detection part
 			//Generate windows and filter by variance
@@ -491,10 +327,6 @@ namespace cv
 							continue;
 						varBuffer.push_back(Point(dx * i, dy * j));
 						varScaleIDs.push_back(scaleID);
-
-						//Debug display candidates after Variance Filter
-						double curScale = pow(tld::SCALE_STEP, scaleID);
-						debugStack[0].push_back(Rect2d(dx * i* curScale, dy * j*curScale, initSize.width*curScale, initSize.height*curScale));
 					}
 				}
 				scaleID++;
@@ -506,12 +338,8 @@ namespace cv
 				GaussianBlur(resized_imgs[scaleID], tmp, GaussBlurKernelSize, 0.0f);
 				blurred_imgs.push_back(tmp);
 			} while (size.width >= initSize.width && size.height >= initSize.height);
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Variance: %d\t%f\n", varBuffer.size(), t);
 
 			//Encsemble classification
-			//e1 = getTickCount();
 			for (int i = 0; i < (int)varBuffer.size(); i++)
 			{
 				prepareClassifiers(static_cast<int> (blurred_imgs[varScaleIDs[i]].step[0]));
@@ -520,15 +348,8 @@ namespace cv
 				ensBuffer.push_back(varBuffer[i]);
 				ensScaleIDs.push_back(varScaleIDs[i]);
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Ensemble: %d\t%f\n", ensBuffer.size(), t);
-
-			//printf("varBuffer: %d\n", varBuffer.size());
-			//printf("ensBuffer: %d\n", ensBuffer.size());
 
 			//NN classification
-			//e1 = getTickCount();
 			for (int i = 0; i < (int)ensBuffer.size(); i++)
 			{
 				LabeledPatch labPatch;
@@ -562,9 +383,6 @@ namespace cv
 					maxScRect = labPatch.rect;
 				}
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("NN: %d\t%f\n", patches.size(), t);
 
 			if (maxSc < 0)
 				return false;
@@ -590,10 +408,7 @@ namespace cv
 			std::vector <Mat> resized_imgs, blurred_imgs;
 			std::vector <Point> varBuffer, ensBuffer;
 			std::vector <int> varScaleIDs, ensScaleIDs;
-			//int64 e1, e2;
-			//double t;
 
-			//e1 = getTickCount();
 			//Detection part
 			//Generate windows and filter by variance
 			scaleID = 0;
@@ -622,12 +437,8 @@ namespace cv
 				GaussianBlur(resized_imgs[scaleID], tmp, GaussBlurKernelSize, 0.0f);
 				blurred_imgs.push_back(tmp);
 			} while (size.width >= initSize.width && size.height >= initSize.height);
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Variance: %d\t%f\n", varBuffer.size(), t);
 
 			//Encsemble classification
-			//e1 = getTickCount();
 			for (int i = 0; i < (int)varBuffer.size(); i++)
 			{
 				prepareClassifiers((int)blurred_imgs[varScaleIDs[i]].step[0]);
@@ -636,12 +447,8 @@ namespace cv
 				ensBuffer.push_back(varBuffer[i]);
 				ensScaleIDs.push_back(varScaleIDs[i]);
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("Ensemble: %d\t%f\n", ensBuffer.size(), t);
 
 			//NN classification
-			//e1 = getTickCount();
 			//Prepare batch of patches
 			int numOfPatches = (int)ensBuffer.size();
 			Mat_<uchar> stdPatches(numOfPatches, 225);
@@ -671,9 +478,6 @@ namespace cv
 
 				srValue = resultSr[i];
 
-				//srValue = Sr(standardPatch);
-				//printf("%f\t%f\t\n", srValue, resultSr[i]);
-
 				////To fix: Check the paper, probably this cause wrong learning
 				//
 				labPatch.isObject = srValue > THETA_NN;
@@ -697,9 +501,6 @@ namespace cv
 					maxScRect = labPatch.rect;
 				}
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency()*1000.0;
-			//printf("NN: %d\t%f\n", patches.size(), t);
 
 			if (maxSc < 0)
 				return false;
diff --git a/modules/tracking/src/tldDetector.hpp b/modules/tracking/src/tldDetector.hpp
index 6e8878b01..7f5a3f9e5 100644
--- a/modules/tracking/src/tldDetector.hpp
+++ b/modules/tracking/src/tldDetector.hpp
@@ -81,7 +81,6 @@ namespace cv
 			double ocl_Sc(const Mat_<uchar>& patch);
 			void ocl_batchSrSc(const Mat_<uchar>& patches, double *resultSr, double *resultSc, int numOfPatches);
 
-			std::vector <Rect2d> debugStack[10];
 			std::vector<TLDEnsembleClassifier> classifiers;
 			Mat *posExp, *negExp;
 			int *posNum, *negNum;
diff --git a/modules/tracking/src/tldModel.cpp b/modules/tracking/src/tldModel.cpp
index 085402265..9415c2a99 100644
--- a/modules/tracking/src/tldModel.cpp
+++ b/modules/tracking/src/tldModel.cpp
@@ -140,7 +140,6 @@ namespace cv
 						detector->classifiers[k].integrate(blurredPatch, false);
 				}
 			}
-			//dprintf(("positive patches: %d\nnegative patches: %d\n", (int)positiveExamples.size(), (int)negativeExamples.size()));
 		}
 
 
@@ -180,16 +179,6 @@ namespace cv
 						detector->classifiers[i].integrate(blurredPatch, patches[k].isObject);
 				}
 			}
-			/*
-			if( negativeIntoModel > 0 )
-			dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
-			if( positiveIntoModel > 0)
-			dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
-			if( negativeIntoEnsemble > 0 )
-			dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
-			if( positiveIntoEnsemble > 0 )
-			dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
-			dfprintf((stdout, "\n"));*/
 
 		}
 
@@ -198,9 +187,6 @@ namespace cv
 			int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
 			if ((int)eForModel.size() == 0) return;
 
-			//int64 e1, e2;
-			//double t;
-			//e1 = getTickCount();
 			for (int k = 0; k < (int)eForModel.size(); k++)
 			{
 				double sr = detector->Sr(eForModel[k]);
@@ -231,19 +217,6 @@ namespace cv
 						detector->classifiers[i].integrate(eForEnsemble[k], isPositive);
 				}
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency() * 1000;
-			//printf("Integrate Additional: %fms\n", t);
-			/*
-			if( negativeIntoModel > 0 )
-			dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
-			if( positiveIntoModel > 0 )
-			dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
-			if( negativeIntoEnsemble > 0 )
-			dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
-			if( positiveIntoEnsemble > 0 )
-			dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
-			dfprintf((stdout, "\n"));*/
 		}
 
 		void TrackerTLDModel::ocl_integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive)
@@ -251,10 +224,6 @@ namespace cv
 			int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
 			if ((int)eForModel.size() == 0) return;
 
-			//int64 e1, e2;
-			//double t;
-			//e1 = getTickCount();
-
 			//Prepare batch of patches
 			int numOfPatches = (int)eForModel.size();
 			Mat_<uchar> stdPatches(numOfPatches, 225);
@@ -301,19 +270,6 @@ namespace cv
 						detector->classifiers[i].integrate(eForEnsemble[k], isPositive);
 				}
 			}
-			//e2 = getTickCount();
-			//t = (e2 - e1) / getTickFrequency() * 1000;
-			//printf("Integrate Additional OCL: %fms\n", t);
-			/*
-			if( negativeIntoModel > 0 )
-			dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
-			if( positiveIntoModel > 0 )
-			dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
-			if( negativeIntoEnsemble > 0 )
-			dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
-			if( positiveIntoEnsemble > 0 )
-			dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
-			dfprintf((stdout, "\n"));*/
 		}
 
 		//Push the patch to the model
diff --git a/modules/tracking/src/tldTracker.cpp b/modules/tracking/src/tldTracker.cpp
index 5483fe336..a1334a91c 100644
--- a/modules/tracking/src/tldTracker.cpp
+++ b/modules/tracking/src/tldTracker.cpp
@@ -151,12 +151,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
     }
     std::vector<double>::iterator it = std::max_element(candidatesRes.begin(), candidatesRes.end());
 
-    //dfprintf((stdout, "scale = %f\n", log(1.0 * boundingBox.width / (data->getMinSize()).width) / log(SCALE_STEP)));
-    //for( int i = 0; i < (int)candidatesRes.size(); i++ )
-        //dprintf(("\tcandidatesRes[%d] = %f\n", i, candidatesRes[i]));
-    //data->printme();
-    //tldModel->printme(stdout);
-
     if( it == candidatesRes.end() )
     {
         data->confident = false;
@@ -173,16 +167,7 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
 
 #if 1
     if( it != candidatesRes.end() )
-    {
         resample(imageForDetector, candidates[it - candidatesRes.begin()], standardPatch);
-        //dfprintf((stderr, "%d %f %f\n", data->frameNum, tldModel->Sc(standardPatch), tldModel->Sr(standardPatch)));
-        //if( candidatesRes.size() == 2 &&  it == (candidatesRes.begin() + 1) )
-            //dfprintf((stderr, "detector WON\n"));
-    }
-    else
-    {
-        //dfprintf((stderr, "%d x x\n", data->frameNum));
-    }
 #endif
 
     if( *it > CORE_THRESHOLD )
@@ -213,7 +198,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
             detectorResults[i].isObject = expertResult;
         }
         tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults);
-        //dprintf(("%d relabeled by nExpert\n", negRelabeled));
         pExpert.additionalExamples(examplesForModel, examplesForEnsemble);
 		if (ocl::haveOpenCL())
 			tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, true);
@@ -234,16 +218,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
 #endif
     }
 
-
-	//Debug display candidates after Variance Filter
-	////////////////////////////////////////////////
-	Mat tmpImg = image;
-	for (int i = 0; i < (int)tldModel->detector->debugStack[0].size(); i++)
-		//rectangle(tmpImg, tldModel->detector->debugStack[0][i], Scalar(255, 255, 255), 1, 1, 0);
-	tldModel->detector->debugStack[0].clear();
-	tmpImg.copyTo(image);
-
-	////////////////////////////////////////////////
     return true;
 }
 
@@ -310,7 +284,6 @@ Data::Data(Rect2d initBox)
     minSize.width = (int)(initBox.width * 20.0 / minDim);
     minSize.height = (int)(initBox.height * 20.0 / minDim);
     frameNum = 0;
-    //dprintf(("minSize = %dx%d\n", minSize.width, minSize.height));
 }
 
 void Data::printme(FILE*  port)