Simplified validator model.

lib/local/LandmarkDetector/include/LandmarkDetectorModel.h

@@ -124,7 +124,6 @@ public:
 	// Keeping track of how many frames the tracker has failed in so far when tracking in videos
 	// This is useful for knowing when to initialise and reinitialise tracking
 	int failures_in_a_row;
-	int success_in_a_row;
 
 	// A template of a face that last succeeded with tracking (useful for large motions in video)
 	cv::Mat_<uchar> face_template;

lib/local/LandmarkDetector/src/LandmarkDetectionValidator.cpp

@@ -887,8 +887,7 @@ double DetectionValidator::CheckCNN_tbb(const cv::Mat_<double>& warped_img, int
 					outputs[0] = outputs[0] + output;
 				}
 
-				if(cnn_convolutional_layers[view_id][cnn_layer][0].size() > 20)
+
-				{
 				// TBB pass for the remaining kernels, empirically helps with layers with more kernels
 				tbb::parallel_for(1, (int)cnn_convolutional_layers[view_id][cnn_layer][in].size(), [&](int k) {
 				{
@@ -923,42 +922,7 @@ double DetectionValidator::CheckCNN_tbb(const cv::Mat_<double>& warped_img, int
 					}
 				}
 				});
-				}
-				else
-				{
-					for (size_t k = 1; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
-					{
-						cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
 				
-						// The convolution (with precomputation)
-						cv::Mat_<float> output;
-						if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) // This will only be needed during the first pass
-						{
-							std::map<int, cv::Mat_<double> > precomputed_dft;
-
-							LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
-
-							cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
-							cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
-						}
-						else
-						{
-							std::map<int, cv::Mat_<double> > precomputed_dft;
-							precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
-							LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
-						}
-
-						// Combining the maps
-						if (in == 0)
-						{
-							outputs[k] = output;
-						}
-						else
-						{
-							outputs[k] = outputs[k] + output;
-						}
-					}
-				}
 			}
 
 			for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)

lib/local/LandmarkDetector/src/LandmarkDetectorFunc.cpp

@@ -288,36 +288,17 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 			CorrectGlobalParametersVideo(grayscale_image, clnf_model, params);
 		}
 
-
-		// If we are performing face validation, do it every 3 frames due to performance
-		bool reset_to_true = false;
-		double old_certainty = 0;
-		if (params.validate_detections == true && clnf_model.success_in_a_row % 3 != 0)
-		{
-			params.validate_detections = false;
-			reset_to_true = true;
-			old_certainty = clnf_model.detection_certainty;
-		}
-
 		bool track_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);
 		
-		if (reset_to_true)
-		{
-			params.validate_detections = true;
-			clnf_model.detection_certainty = old_certainty;
-		}
-
 		if(!track_success)
 		{
 			// Make a record that tracking failed
 			clnf_model.failures_in_a_row++;
-			clnf_model.success_in_a_row = 0;
 		}
 		else
 		{
 			// indicate that tracking is a success
 			clnf_model.failures_in_a_row = -1;			
-			clnf_model.success_in_a_row++;
 			UpdateTemplate(grayscale_image, clnf_model);
 		}
 	}
@@ -398,7 +379,6 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 			else
 			{
 				clnf_model.failures_in_a_row = -1;			
-				clnf_model.success_in_a_row++;
 				UpdateTemplate(grayscale_image, clnf_model);
 				return true;
 			}
@@ -409,14 +389,12 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 	if(!clnf_model.tracking_initialised)
 	{
 		clnf_model.failures_in_a_row++;
-		clnf_model.success_in_a_row = 0;
 	}
 
 	// un-initialise the tracking
 	if(	clnf_model.failures_in_a_row > 100)
 	{
 		clnf_model.tracking_initialised = false;
-		clnf_model.success_in_a_row = 0;
 	}
 
 	return clnf_model.detection_success;

lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp

@@ -76,7 +76,6 @@ CLNF::CLNF(const CLNF& other): pdm(other.pdm), params_local(other.params_local.c
 	this->detection_certainty = other.detection_certainty;
 	this->model_likelihood = other.model_likelihood;
 	this->failures_in_a_row = other.failures_in_a_row;
-	this->success_in_a_row = other.success_in_a_row;
 
 	// Load the CascadeClassifier (as it does not have a proper copy constructor)
 	if(!face_detector_location.empty())
@@ -122,7 +121,6 @@ CLNF & CLNF::operator= (const CLNF& other)
 		this->detection_certainty = other.detection_certainty;
 		this->model_likelihood = other.model_likelihood;
 		this->failures_in_a_row = other.failures_in_a_row;
-		this->success_in_a_row = other.success_in_a_row;
 
 		this->eye_model = other.eye_model;
 
@@ -166,7 +164,6 @@ CLNF::CLNF(const CLNF&& other)
 	this->detection_certainty = other.detection_certainty;
 	this->model_likelihood = other.model_likelihood;
 	this->failures_in_a_row = other.failures_in_a_row;
-	this->success_in_a_row = other.success_in_a_row;
 
 	pdm = other.pdm;
 	params_local = other.params_local;
@@ -202,7 +199,6 @@ CLNF & CLNF::operator= (const CLNF&& other)
 	this->detection_certainty = other.detection_certainty;
 	this->model_likelihood = other.model_likelihood;
 	this->failures_in_a_row = other.failures_in_a_row;
-	this->success_in_a_row = other.success_in_a_row;
 
 	pdm = other.pdm;
 	params_local = other.params_local;
@@ -531,7 +527,6 @@ void CLNF::Read(string main_location)
 	params_global = cv::Vec6d(1, 0, 0, 0, 0, 0);
 
 	failures_in_a_row = -1;
-	success_in_a_row = 0;
 
 }
 
@@ -552,7 +547,6 @@ void CLNF::Reset()
 	params_global = cv::Vec6d(1, 0, 0, 0, 0, 0);
 
 	failures_in_a_row = -1;
-	success_in_a_row = 0;
 	face_template = cv::Mat_<uchar>();
 }
 

matlab_runners/Head Pose Experiments/results/Pose_OF.txt

@@ -1,4 +1,4 @@
 Dataset and model,        pitch,  yaw,  roll,  mean,  median
-biwi error:  7.955,   5.583, 4.402,  5.980,  2.624
+biwi error:  6.870,   5.338, 4.482,  5.563,  2.632
-bu error:    2.762,   4.103, 2.568,  3.145,  2.118
+bu error:    2.785,   4.117, 2.571,  3.158,  2.119
-ict error:   3.620,   3.608, 3.626,  3.618,  2.028
+ict error:   3.481,   3.641, 3.581,  3.568,  2.036