Added optimization to Multi-target TLD update

modules/tracking/include/opencv2/tracking/tracker.hpp

@@ -565,7 +565,7 @@ class CV_EXPORTS_W Tracker : public virtual Algorithm
   virtual void read( const FileNode& fn )=0;
   virtual void write( FileStorage& fs ) const=0;
 
- protected:
+ public:
 
   virtual bool initImpl( const Mat& image, const Rect2d& boundingBox ) = 0;
   virtual bool updateImpl( const Mat& image, Rect2d& boundingBox ) = 0;

modules/tracking/src/multiTracker.cpp

@@ -1,4 +1,4 @@
-#include <precomp.hpp>
+#include "tldTracker.hpp"
 
 namespace cv
 {
@@ -35,14 +35,156 @@ namespace cv
 
 		return true;
 	}
-
+	 
 	//Multitracker TLD
 	/*Optimized update method for TLD Multitracker */
 	bool MultiTrackerTLD::update(const Mat& image)
 	{
-		for (int i = 0; i < trackers.size(); i++)
-			if (!trackers[i]->update(image, boundingBoxes[i]))
+		
+		for (int k = 0; k < trackers.size(); k++)
+		{
+			//Set current target(tracker) parameters
+			Rect2d boundingBox = boundingBoxes[k];
+			Ptr<tld::TrackerTLDImpl> tracker = (Ptr<tld::TrackerTLDImpl>)static_cast<Ptr<tld::TrackerTLDImpl>> (trackers[k]);
+			tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
+			Ptr<tld::Data> data = tracker->data;
+			double scale = data->getScale();
+
+
+			Mat image_gray, image_blurred, imageForDetector;
+			cvtColor(image, image_gray, COLOR_BGR2GRAY);
+
+			if (scale > 1.0)
+				resize(image_gray, imageForDetector, Size(cvRound(image.cols*scale), cvRound(image.rows*scale)), 0, 0, tld::DOWNSCALE_MODE);
+			else
+				imageForDetector = image_gray;
+			GaussianBlur(imageForDetector, image_blurred, tld::GaussBlurKernelSize, 0.0);
+
+			data->frameNum++;
+			Mat_<uchar> standardPatch(tld::STANDARD_PATCH_SIZE, tld::STANDARD_PATCH_SIZE);
+			std::vector<tld::TLDDetector::LabeledPatch> detectorResults;
+			//best overlap around 92%
+
+			std::vector<Rect2d> candidates;
+			std::vector<double> candidatesRes;
+			bool trackerNeedsReInit = false;
+			bool DETECT_FLG = false;
+			for (int i = 0; i < 2; i++)
+			{
+				Rect2d tmpCandid = boundingBox;
+
+				if (i == 1)
+				{
+					if (ocl::haveOpenCL())
+						DETECT_FLG = tldModel->detector->ocl_detect(imageForDetector, image_blurred, tmpCandid, detectorResults, tldModel->getMinSize());
+					else
+						DETECT_FLG = tldModel->detector->detect(imageForDetector, image_blurred, tmpCandid, detectorResults, tldModel->getMinSize());
+				}
+
+				if (((i == 0) && !data->failedLastTime && tracker->trackerProxy->update(image, tmpCandid)) || (DETECT_FLG))
+				{
+					candidates.push_back(tmpCandid);
+					if (i == 0)
+						tld::resample(image_gray, tmpCandid, standardPatch);
+					else
+						tld::resample(imageForDetector, tmpCandid, standardPatch);
+					candidatesRes.push_back(tldModel->detector->Sc(standardPatch));
+				}
+				else
+				{
+					if (i == 0)
+						trackerNeedsReInit = true;
+				}
+			}
+
+			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;
+				data->failedLastTime = true;
 				return false;
+			}
+			else
+			{
+				boundingBox = candidates[it - candidatesRes.begin()];
+				boundingBoxes[k] = boundingBox;
+				data->failedLastTime = false;
+				if (trackerNeedsReInit || it != candidatesRes.begin())
+					tracker->trackerProxy->init(image, boundingBox);
+			}
+
+#if 1
+			if (it != candidatesRes.end())
+			{
+				tld::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 > tld::CORE_THRESHOLD)
+				data->confident = true;
+
+			if (data->confident)
+			{
+				tld::TrackerTLDImpl::Pexpert pExpert(imageForDetector, image_blurred, boundingBox, tldModel->detector, tracker->params, data->getMinSize());
+				tld::TrackerTLDImpl::Nexpert nExpert(imageForDetector, boundingBox, tldModel->detector, tracker->params);
+				std::vector<Mat_<uchar> > examplesForModel, examplesForEnsemble;
+				examplesForModel.reserve(100); examplesForEnsemble.reserve(100);
+				int negRelabeled = 0;
+				for (int i = 0; i < (int)detectorResults.size(); i++)
+				{
+					bool expertResult;
+					if (detectorResults[i].isObject)
+					{
+						expertResult = nExpert(detectorResults[i].rect);
+						if (expertResult != detectorResults[i].isObject)
+							negRelabeled++;
+					}
+					else
+					{
+						expertResult = pExpert(detectorResults[i].rect);
+					}
+
+					detectorResults[i].shouldBeIntegrated = detectorResults[i].shouldBeIntegrated || (detectorResults[i].isObject != expertResult);
+					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);
+				else
+					tldModel->integrateAdditional(examplesForModel, examplesForEnsemble, true);
+				examplesForModel.clear(); examplesForEnsemble.clear();
+				nExpert.additionalExamples(examplesForModel, examplesForEnsemble);
+
+				if (ocl::haveOpenCL())
+					tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, false);
+				else
+					tldModel->integrateAdditional(examplesForModel, examplesForEnsemble, false);
+			}
+			else
+			{
+#ifdef CLOSED_LOOP
+				tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults);
+#endif
+			}
+
+			
+		}
 
 		return true;
 	}

modules/tracking/src/tldTracker.hpp

@@ -128,7 +128,6 @@ public:
 	void read(const FileNode& fn);
 	void write(FileStorage& fs) const;
 
-protected:
 	class Pexpert
 	{
 	public: