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Santhosh Sunderrajan
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| ###################################################### | ||
| # Input Information # | ||
| # Input Information # | ||
| ###################################################### | ||
| Number_of_Frames = 21 # Number of frames to track | ||
| Starting_Frame_Index = 1 # Starting frame index | ||
| Camera_Set = "1" # The set of Cameras | ||
| Object_Set = "1" # The set of objects being tracked, e.g. "1,2" | ||
| Number_of_Frames = 21 # Number of frames to track | ||
| Starting_Frame_Index = 1 # Starting frame index | ||
| Camera_Set = "1" # The set of Cameras | ||
| Object_Set = "1" # The set of objects being tracked, e.g. "1,2" | ||
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| Input_Directory_Name = "TestVideos\" # Root directory address; | ||
| Input_Data_FilesName = "V8" # Directory name to hold data files | ||
| Initialization_Name = "V8" # The local directory (inside the experiment directory) to hold the initialization files; | ||
| # E.g "initialization1", or use "." if same as the homography files | ||
| Load_Video_With_Color = 1 # [0-default]: Load image as gray scale image; [1]: load as RGB color image | ||
| Load_Video_From_Images = 1 # [0]: Load video as image sequences; [1-default]: load from a video file | ||
| Input_Directory_Name = "TestVideos\" # Root directory address; | ||
| Input_Data_FilesName = "V8" # Directory name to hold data files | ||
| Initialization_Name = "V8" # The local directory (inside the experiment directory) to hold the initialization files; | ||
| # E.g "initialization1", or use "." if same as the homography files | ||
| Load_Video_With_Color = 1 # [0-default]: Load image as gray scale image; [1]: load as RGB color image | ||
| Load_Video_From_Images = 1 # [0]: Load video as image sequences; [1-default]: load from a video file | ||
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| ###################################################### | ||
| # Output related setting # | ||
| # Output related setting # | ||
| ###################################################### | ||
| Trial_Number = 1 # Trial number for this particular experiment | ||
| Output_Directory_Name = "OutputVideos\" | ||
| Enable_Verbose_Mode = 1 # [1-default]: Enable verbose mode; [0]: disable | ||
| Enalbe_Detailed_Log = 1 # [1-default]: Enable detailed log; [0]: disable | ||
| Whether_Save_Output_Video = 1 # [0-default]: No; [1]: Yes; Save output video (with tracked blobs) | ||
| Whether_Display_Output_Video = 1 # [0-default]: No; [1]: Yes; Display output video (with tracked blobs) | ||
| Whether_Display_Training_Samples = 1 # [0-default]: No; [1]: Yes | ||
| Whether_Save_Training_Samples = 1 # [0-default]: No; [1]: Yes | ||
| Display_Training_Center_Only = 1 # [0-default]: No; [1]: Yes; Display only the center of the training Samples | ||
| Wait_Before_TrackingEnd = 1 # [1]: Hold the process before exit at the end of tracking; [0-default]:No. | ||
| Trial_Number = 1 # Trial number for this particular experiment | ||
| Output_Directory_Name = "OutputVideos\" | ||
| Enable_Verbose_Mode = 1 # [1-default]: Enable verbose mode; [0]: disable | ||
| Enalbe_Detailed_Log = 1 # [1-default]: Enable detailed log; [0]: disable | ||
| Whether_Save_Output_Video = 1 # [0-default]: No; [1]: Yes; Save output video (with tracked blobs) | ||
| Whether_Display_Output_Video = 1 # [0-default]: No; [1]: Yes; Display output video (with tracked blobs) | ||
| Whether_Display_Training_Samples = 1 # [0-default]: No; [1]: Yes | ||
| Whether_Save_Training_Samples = 1 # [0-default]: No; [1]: Yes | ||
| Display_Training_Center_Only = 1 # [0-default]: No; [1]: Yes; Display only the center of the training Samples | ||
| Wait_Before_TrackingEnd = 1 # [1]: Hold the process before exit at the end of tracking; [0-default]:No. | ||
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| ###################################################### | ||
| # Feature and Classifier for Local Tracker # | ||
| # Feature and Classifier for Local Tracker # | ||
| ###################################################### | ||
| Tracker_Feature_Type = 1 # [1-default]: Haar; [2]: 11-dim Culture Color; [3]: 512-dim MultiDimensional color | ||
| Tracker_Feature_Parameter = 250 # Number of Haar features if Tracker_Feature_Type = 1; Otherwise: ignored | ||
| Tracker_Strong_Classifier_Type = 1 # [1-default]: MilBoost; [2]: AdaBoost; [3]: MilEnsemble | ||
| Tracker_Weak_Classifier_Type = 1 # For MilBoost/AdaBoost, [1-default]: STUMP; [2]: Weighted STUMP; [3]: Perceptron | ||
| # For MilEnsemble, this parameter is ignored, as only percepron is allowed | ||
| Percentage_Of_Weak_Classifiers_Selected = 20 # Percentage of weak classifiers selected from the available weak classifier pool. | ||
| Percentage_Of_Weak_Classifier_Retained = 10 # Applicable for MILEnsemble, should be lesser than Percentage_Of_Weak_Classifiers_Selected | ||
| Tracker_Feature_Type = 1 # [1-default]: Haar; [2]: 11-dim Culture Color; [3]: 512-dim MultiDimensional color | ||
| Tracker_Feature_Parameter = 250 # Number of Haar features if Tracker_Feature_Type = 1; Otherwise: ignored | ||
| Tracker_Strong_Classifier_Type = 1 # [1-default]: MilBoost; [2]: AdaBoost; [3]: MilEnsemble | ||
| Tracker_Weak_Classifier_Type = 1 # For MilBoost/AdaBoost, [1-default]: STUMP; [2]: Weighted STUMP; [3]: Perceptron | ||
| # For MilEnsemble, this parameter is ignored, as only percepron is allowed | ||
| Percentage_Of_Weak_Classifiers_Selected = 20 # Percentage of weak classifiers selected from the available weak classifier pool. | ||
| Percentage_Of_Weak_Classifier_Retained = 10 # Applicable for MILEnsemble, should be lesser than Percentage_Of_Weak_Classifiers_Selected | ||
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| ###################################################### | ||
| # Local Tracker Setttings # | ||
| # Local Tracker Setttings # | ||
| ###################################################### | ||
| Local_Tracker_Type = 0 # [0-default]: Simple Tracker; [1]: Particle Filter Tracker | ||
| Local_Tracker_Type = 0 # [0-default]: Simple Tracker; [1]: Particle Filter Tracker | ||
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| Inner_Radius_For_Positive_Examples = 10 # Value for SimpleTrackerParameters.m_posRadiusTrain, used for the selection | ||
| # of positive training example when applicable; Typical value: 4 pixel for Mil, | ||
| # 1 to 4 for Adaboost (as it is less robust) | ||
| Initial_Radius_For_Positive_Examples = 7 # Default = 3; radius (Number of pixels); | ||
| # All samples within the radius to select positive training examples at the beginning | ||
| Number_Of_Negative_Examples = 30 # Number of negative examples for training | ||
| Initial_Number_Of_Negative_Examples = 30 # Initial number of negative examples for training | ||
| Inner_Radius_For_Positive_Examples = 10 # Value for SimpleTrackerParameters.m_posRadiusTrain, used for the selection | ||
| # of positive training example when applicable; Typical value: 4 pixel for Mil, | ||
| # 1 to 4 for Adaboost (as it is less robust) | ||
| Initial_Radius_For_Positive_Examples = 7 # Default = 3; radius (Number of pixels); | ||
| # All samples within the radius to select positive training examples at the beginning | ||
| Number_Of_Negative_Examples = 30 # Number of negative examples for training | ||
| Initial_Number_Of_Negative_Examples = 30 # Initial number of negative examples for training | ||
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| Search_Window_Size = 20 # Applicable to simple tracker only, | ||
| # Also used by particle filter during initialization | ||
| Negative_Sampling_Strategy = 1 # Simple Tracker Samplin Strategy | ||
| #[0]: all over image (not recommended) | ||
| #[1 - default]: close to the search window | ||
| # Negative samples are sampled inside 1.5*Search_Window_Size and outside 5 + "Inner_Radius_For_Positive_Examples" | ||
| Search_Window_Size = 20 # Applicable to simple tracker only, | ||
| # Also used by particle filter during initialization | ||
| Negative_Sampling_Strategy = 1 # Simple Tracker Samplin Strategy | ||
| #[0]: all over image (not recommended) | ||
| #[1 - default]: close to the search window | ||
| # Negative samples are sampled inside 1.5*Search_Window_Size and outside 5 + "Inner_Radius_For_Positive_Examples" | ||
| ###################################################### | ||
| # Particle Filters for Local Tracker # | ||
| # Particle Filters for Local Tracker # | ||
| ###################################################### | ||
| Num_Of_Particles = 1000 # Number of particles | ||
| Particle_Filter_Std_Dev_X = 20 # Value for ParticleFilterTrackerParameters.m_standardDeviationX in pixels | ||
| Particle_Filter_Std_Dev_Y = 20 # Value for ParticleFilterTrackerParameters.m_standardDeviationY in pixels | ||
| Particle_Filter_Std_Dev_ScaleX = 0.0000001 # Value for ParticleFilterTrackerParameters.m_standardDeviationScaleX =0, i.e. no scale change | ||
| Particle_Filter_Std_Dev_ScaleY = 0.0000001 # Value for ParticleFilterTrackerParameters.m_standardDeviationScaleY =0, i.e. no scale change | ||
| PfTracker_Max_Num_Positive_Examples = 30 # Maximum number of positive examples generative, (typically less than the number of Particles) | ||
| # Actual number could be different depends on the sampling strategy. | ||
| PfTracker_Num_Disp_Particles = 5 # Number of particle blobs displayed on the video if applicable | ||
| PfTracker_Output_Trajectory_Option = 0 # [0]: The particle of highest weight; [1-default]: average of all particles | ||
| PfTracker_Positive_Example_Strategy = 0 # [0-default]:Generate examples in the same way as simpleTracker based on the particle average | ||
| # (or the particle of highest weight if PfTracker_Output_Trajectory_Option = 0) | ||
| # [1]: Use all unique particles (weight descending order) until reaching maximum | ||
| # [2]: Same as [1]. Except when When there are not enough unique particles, | ||
| # pick all unique particles, and sample the rest similar to [0] | ||
| # [3]: Random sampling from the re-sampled particles regardless of same sample | ||
| PfTracker_Negative_Example_Strategy = 0 # [0-default]: Generate examples in the same way as simpleTracker based on the particle average | ||
| # (or the particle of highest weight if PfTracker_Output_Trajectory_Option = 0) | ||
| # That is inside 1.5*Search_Window_Size and outside 5 + "Inner_Radius_For_Positive_Examples" | ||
| # (or all over image if Negative_Sampling_Strategy = 0 (not recommended)); | ||
| # All samples' scale decides by the average scale of all particles | ||
| # [1]: Sample between two circles: outside circle radium 1.5*Search_Window_Size, | ||
| # inside circle radius decided by particles. | ||
| Num_Of_Particles = 1000 # Number of particles | ||
| Particle_Filter_Std_Dev_X = 20 # Value for ParticleFilterTrackerParameters.m_standardDeviationX in pixels | ||
| Particle_Filter_Std_Dev_Y = 20 # Value for ParticleFilterTrackerParameters.m_standardDeviationY in pixels | ||
| Particle_Filter_Std_Dev_ScaleX = 0.0000001 # Value for ParticleFilterTrackerParameters.m_standardDeviationScaleX =0, i.e. no scale change | ||
| Particle_Filter_Std_Dev_ScaleY = 0.0000001 # Value for ParticleFilterTrackerParameters.m_standardDeviationScaleY =0, i.e. no scale change | ||
| PfTracker_Max_Num_Positive_Examples = 30 # Maximum number of positive examples generative, (typically less than the number of Particles) | ||
| # Actual number could be different depends on the sampling strategy. | ||
| PfTracker_Num_Disp_Particles = 5 # Number of particle blobs displayed on the video if applicable | ||
| PfTracker_Output_Trajectory_Option = 0 # [0]: The particle of highest weight; [1-default]: average of all particles | ||
| PfTracker_Positive_Example_Strategy = 0 # [0-default]:Generate examples in the same way as simpleTracker based on the particle average | ||
| # (or the particle of highest weight if PfTracker_Output_Trajectory_Option = 0) | ||
| # [1]: Use all unique particles (weight descending order) until reaching maximum | ||
| # [2]: Same as [1]. Except when When there are not enough unique particles, | ||
| # pick all unique particles, and sample the rest similar to [0] | ||
| # [3]: Random sampling from the re-sampled particles regardless of same sample | ||
| PfTracker_Negative_Example_Strategy = 0 # [0-default]: Generate examples in the same way as simpleTracker based on the particle average | ||
| # (or the particle of highest weight if PfTracker_Output_Trajectory_Option = 0) | ||
| # That is inside 1.5*Search_Window_Size and outside 5 + "Inner_Radius_For_Positive_Examples" | ||
| # (or all over image if Negative_Sampling_Strategy = 0 (not recommended)); | ||
| # All samples' scale decides by the average scale of all particles | ||
| # [1]: Sample between two circles: outside circle radium 1.5*Search_Window_Size, | ||
| # inside circle radius decided by particles. | ||
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| ###################################################### | ||
| # Fusion related Setttings # | ||
| # Fusion related Setttings # | ||
| ###################################################### | ||
| Geometric_Fusion_Type = 0 # [0-default]: No Ground Fusion; [1]: Ground plane Fusion with GMM and Particle Reweighting | ||
| Appearance_Fusion_Type = 0 # [0-default]: No Appearance Fusion; [1]: Culture Color; [2]: MultiDimensional color | ||
| Appearance_Fusion_Strong_Classifier_Type = 1 # [1-default]: MilBoost; [2] = AdaBoost; [3] = MIL_Ensemble | ||
| Appearance_Fusion_Weak_Classifier_Type = 1 # For MilBoost/AdaBoost, [1-default]: STUMP; [2]: Weighted STUMP; [3]: Perceptron | ||
| # For MilEnsemble, this parameter is ignored, as only Perceptron is allowed | ||
| Percentage_Of_Weak_Classifiers_Selected_AF = 20 # Percentage of weak classifiers selected from the available weak classifier pool. | ||
| Percentage_Of_Weak_Classifier_Retained_AF = 10 # Applicable for MILEnsemble, should be lesser than Percentage_Of_Weak_Classifiers_Selected | ||
| Geometric_Fusion_Type = 0 # [0-default]: No Ground Fusion; [1]: Ground plane Fusion with GMM and Particle Reweighting | ||
| Appearance_Fusion_Type = 0 # [0-default]: No Appearance Fusion; [1]: Culture Color; [2]: MultiDimensional color | ||
| Appearance_Fusion_Strong_Classifier_Type = 1 # [1-default]: MilBoost; [2] = AdaBoost; [3] = MIL_Ensemble | ||
| Appearance_Fusion_Weak_Classifier_Type = 1 # For MilBoost/AdaBoost, [1-default]: STUMP; [2]: Weighted STUMP; [3]: Perceptron | ||
| # For MilEnsemble, this parameter is ignored, as only Perceptron is allowed | ||
| Percentage_Of_Weak_Classifiers_Selected_AF = 20 # Percentage of weak classifiers selected from the available weak classifier pool. | ||
| Percentage_Of_Weak_Classifier_Retained_AF = 10 # Applicable for MILEnsemble, should be lesser than Percentage_Of_Weak_Classifiers_Selected | ||
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| ###################################################### | ||
| # Multi Object Interaction # | ||
| # Multi Object Interaction # | ||
| ###################################################### | ||
| Enable_Cross_Camera_Occlusion_Handle = 0 #[0-default]: No; 1: Yes | ||
| Enable_Cross_Camera_Auto_Initialization = 0 #[0-default]: No; 1: Yes | ||
| Enable_Cross_Camera_Occlusion_Handle = 0 # [0-default]: No; 1: Yes | ||
| Enable_Cross_Camera_Auto_Initialization = 0 # [0-default]: No; 1: Yes |
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