Adds boosting tracker

src/ImageTracking.jl

@@ -10,21 +10,25 @@ using StaticArrays
 include("core.jl")
 include("optical_flow.jl")
 include("haar.jl")
+include("tracker.jl")
 
 export
 
 	# main functions
-  optical_flow,
+	optical_flow,
+	init_tracker,
+	update_tracker,
 
 	# other functions
+	polynomial_expansion,
 	haar_coordinates,
 	haar_features,
 
-	# other functions
-	polynomial_expansion,
-
 	# optical flow algorithms
 	LK,
-	Farneback
+	Farneback,
+
+	#tracking algorithms
+	TrackerBoosting
 
 end

src/boosting_tracker/boosting_tracker.jl

@@ -0,0 +1,156 @@
+mutable struct TrackerBoosting{I <: Int, F <: Float64} <: Tracker
+    #Initialized
+    num_of_classifiers::I
+    sampler_overlap::F
+    sampler_search_factor::F
+    initial_iterations::I
+    num_of_features::I
+
+    #Uninitialized
+    haar_tracker_features::HaarTrackerFeatures
+    sampler::TrackerSampler
+    model::TrackerModel
+
+    TrackerBoosting(num_of_classifiers::I = 100, sampler_overlap::F = 0.99, sampler_search_factor::F = 1.8, initial_iterations::I = 20, num_of_features::I = 1050) where {I <: Int, F <: Float64} = new{I, F}(
+                    num_of_classifiers, sampler_overlap, sampler_search_factor, initial_iterations, num_of_features)
+end
+
+function init_impl(tracker::TrackerBoosting, image::Array{T, 2}, bounding_box::MVector{4, Int}) where T
+	srand(1)
+
+    int_image = integral_image(Gray.(image))
+
+    #Sampling
+    tracker.sampler = TS_Current_Sample(tracker.sampler_overlap, tracker.sampler_search_factor, :positive)
+    positive_samples = sample_impl(tracker.sampler, int_image, bounding_box)
+
+    tracker.sampler.mode = :negative
+    negative_samples = sample_impl(tracker.sampler, int_image, bounding_box)
+
+    if isempty(positive_samples) || isempty(negative_samples)
+        throw(ArgumentError("Could not get initial samples."))
+    end
+
+    ROI = tracker.sampler.sampling_ROI
+    #Compute Haar Features
+    tracker.haar_tracker_features = HaarTrackerFeatures(tracker.num_of_features, MVector{2}((bounding_box[3] - bounding_box[1] + 1), (bounding_box[4] - bounding_box[2] + 1)))
+    generate_features(tracker.haar_tracker_features, tracker.num_of_features)
+
+    extraction(tracker.haar_tracker_features, MVector{length(positive_samples)}(positive_samples))
+    pos_responses = tracker.haar_tracker_features.responses
+    extraction(tracker.haar_tracker_features, MVector{length(negative_samples)}(negative_samples))
+    neg_responses = tracker.haar_tracker_features.responses
+
+    #Model
+    tracker.model = initialize_boosting_model(bounding_box)
+    state_estimator = TSE_Adaboost(tracker.num_of_classifiers, tracker.initial_iterations, tracker.num_of_features, false, MVector{2}((bounding_box[3] - bounding_box[1] + 1), (bounding_box[4] - bounding_box[2] + 1)), ROI)
+    tracker.model.state_estimator = state_estimator
+
+    #Run model estimation and update for initial_iterations iterations
+    for i = 1:tracker.initial_iterations
+        #Compute temp features
+        temp_haar_features = HaarTrackerFeatures(length(positive_samples)+length(negative_samples), MVector{2}((bounding_box[3] - bounding_box[1] + 1), (bounding_box[4] - bounding_box[2] + 1)))
+        generate_features(temp_haar_features, temp_haar_features.num_of_features)
+
+        #Model estimate
+        tracker.model.mode = :negative
+        tracker.model.current_sample = MVector{length(negative_samples)}(negative_samples)
+        model_estimation(tracker.model, neg_responses, true)
+
+        tracker.model.mode = :positive
+        tracker.model.current_sample = MVector{length(positive_samples)}(positive_samples)
+        model_estimation(tracker.model, pos_responses, true)
+
+        #Model update
+        model_update(tracker.model)
+
+        #Get replaced classifier and change the features
+        replaced_classifier = tracker.model.state_estimator.replaced_classifier
+        swapped_classifier = tracker.model.state_estimator.swapped_classifier
+
+        for j = 1:length(replaced_classifier)
+            if replaced_classifier[j] > 0 && swapped_classifier[j] > 0
+                swap_feature(tracker.haar_tracker_features, replaced_classifier[j], swapped_classifier[j])
+                swap_feature(tracker.haar_tracker_features, swapped_classifier[j], temp_haar_features.features[j])
+            end
+        end
+    end
+end
+
+function update_impl(tracker::TrackerBoosting, image::Array{T, 2}) where T
+    srand(1)
+    int_image = integral_image(Gray.(image))
+
+    #Get the last location
+    last_target_state = tracker.model.trajectory[end]
+    last_target_bounding_box = MVector{4}(round(Int, last_target_state.position[1]), round(Int, last_target_state.position[2]), round(Int, last_target_state.position[1])+last_target_state.height-1, round(Int, last_target_state.position[2])+last_target_state.width-1)
+
+    #Sampling new frame based on last location
+    tracker.sampler.mode = :classify
+    detection_samples = sample_impl(tracker.sampler, int_image, last_target_bounding_box)
+    ROI = tracker.sampler.sampling_ROI
+
+    if isempty(detection_samples)
+        throw(ArgumentError("Could not get detection samples."))
+    end
+
+    classifiers = get_selected_weak_classifier(tracker.model.state_estimator.boost_classifier)
+    extractor = tracker.haar_tracker_features
+    responses = extract_selected(extractor, MVector{length(classifiers)}(classifiers), MVector{length(detection_samples)}(detection_samples))
+
+    #Predict new location
+    tracker.model.mode = :classify
+    tracker.model.current_sample = MVector{length(detection_samples)}(detection_samples)
+    tracker.model.state_estimator.current_confidence_map = model_estimation(tracker.model, responses, false)
+    tracker.model.state_estimator.sample_ROI = ROI
+
+    run_state_estimator(tracker.model)
+    current_state = tracker.model.trajectory[end]
+    new_bounding_box = MVector{4}(round(Int, current_state.position[1]), round(Int, current_state.position[2]), round(Int, current_state.position[1])+current_state.height, round(Int, current_state.position[2])+current_state.width)
+
+    #Sampling new frame based on new location
+    tracker.sampler.mode = :positive
+    positive_samples = sample_impl(tracker.sampler, int_image, new_bounding_box)
+
+    tracker.sampler.mode = :negative
+    negative_samples = sample_impl(tracker.sampler, int_image, new_bounding_box)
+
+    if isempty(positive_samples) || isempty(negative_samples)
+        throw(ArgumentError("Could not get initial samples."))
+    end
+
+    #Extract features
+    extraction(tracker.haar_tracker_features, MVector{length(positive_samples)}(positive_samples))
+    pos_responses = tracker.haar_tracker_features.responses
+    extraction(tracker.haar_tracker_features, MVector{length(negative_samples)}(negative_samples))
+    neg_responses = tracker.haar_tracker_features.responses
+
+    #Compute temp features
+    temp_haar_features = HaarTrackerFeatures(length(positive_samples)+length(negative_samples), MVector{2}((new_bounding_box[3] - new_bounding_box[1] + 1), (new_bounding_box[4] - new_bounding_box[2] + 1)))
+    generate_features(temp_haar_features, temp_haar_features.num_of_features)
+
+    #Model estimate
+    tracker.model.mode = :negative
+    tracker.model.current_sample = MVector{length(negative_samples)}(negative_samples)
+    model_estimation(tracker.model, neg_responses, true)
+
+    tracker.model.mode = :positive
+    tracker.model.current_sample = MVector{length(positive_samples)}(positive_samples)
+    model_estimation(tracker.model, pos_responses, true)
+
+    #Model update
+    model_update(tracker.model)
+
+    #Get replaced classifier and change the features
+    replaced_classifier = tracker.model.state_estimator.replaced_classifier
+    swapped_classifier = tracker.model.state_estimator.swapped_classifier
+
+    for j = 1:length(replaced_classifier)
+        if replaced_classifier[j] > 0 && swapped_classifier[j] > 0
+            swap_feature(tracker.haar_tracker_features, replaced_classifier[j], swapped_classifier[j])
+            swap_feature(tracker.haar_tracker_features, swapped_classifier[j], temp_haar_features.features[j])
+        end
+    end
+
+    return new_bounding_box
+end