Added initial version of the learning to track example program.

examples/CMakeLists.txt

@@ -47,6 +47,7 @@ add_example(krls_ex)
 add_example(krls_filter_ex)
 add_example(krr_classification_ex)
 add_example(krr_regression_ex)
+add_example(learning_to_track_ex)
 add_example(least_squares_ex)
 add_example(linear_manifold_regularizer_ex)
 add_example(logger_ex)

examples/learning_to_track_ex.cpp

+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+
+*/
+
+
+#include <iostream>
+#include <dlib/svm_threaded.h>
+#include <dlib/rand.h>
+
+using namespace std;
+using namespace dlib;
+
+// ----------------------------------------------------------------------------------------
+
+class detection
+{
+
+public:
+    typedef class track track_type;
+
+    matrix<double,0,1> measurements;
+};
+
+class track
+{
+
+public:
+    // This type should be a dlib::matrix capable of storing column vectors or an
+    // unsorted sparse vector type as defined in dlib/svm/sparse_vector_abstract.h.
+    typedef matrix<double,0,1> feature_vector_type;
+
+    track()
+    {
+        time_since_last_association = 0;
+    }
+
+    void get_similarity_features (
+        const detection& det,
+        feature_vector_type& feats
+    ) const
+    {
+        feats = abs(last_measurements - det.measurements);
+    }
+
+    void update_track (
+        const detection& det
+    )
+    {
+        last_measurements = det.measurements;
+        time_since_last_association = 0;
+    }
+
+    void propagate_track (
+    )
+    {
+        ++time_since_last_association;
+    }
+
+    matrix<double,0,1> last_measurements;
+    unsigned long time_since_last_association;
+};
+
+// ----------------------------------------------------------------------------------------
+
+typedef std::vector<labeled_detection<detection> > detections_at_single_time_step;
+typedef std::vector<detections_at_single_time_step> track_history;
+
+dlib::rand rnd;
+const long num_objects = 4;
+const long num_properties = 6;
+std::vector<matrix<double,0,1> > object_properties(num_objects);
+
+void initialize_object_properties()
+{
+    for (unsigned long i = 0; i < object_properties.size(); ++i)
+        object_properties[i] = randm(num_properties,1,rnd);
+}
+
+// ----------------------------------------------------------------------------------------
+
+track_history make_random_tracking_data_for_training()
+{
+    track_history data;
+
+
+    const int num_time_steps = 100;
+    for (int i = 0; i < num_time_steps; ++i)
+    {
+        detections_at_single_time_step dets(3);
+        dets[0].det.measurements = object_properties[0] + randm(num_properties,1,rnd)*0.1;
+        dets[0].label = 0;
+
+        dets[1].det.measurements = object_properties[1] + randm(num_properties,1,rnd)*0.1;
+        dets[1].label = 1;
+
+        dets[2].det.measurements = object_properties[2] + randm(num_properties,1,rnd)*0.1;
+        dets[2].label = 2;
+
+        data.push_back(dets);
+    }
+
+    for (int i = 0; i < num_time_steps; ++i)
+    {
+        detections_at_single_time_step dets(2);
+        dets[0].det.measurements = object_properties[0] + randm(num_properties,1,rnd)*0.1;
+        dets[0].label = 0;
+
+        dets[1].det.measurements = object_properties[3] + randm(num_properties,1,rnd)*0.1;
+        dets[1].label = 3;
+
+        data.push_back(dets);
+    }
+
+    return data;
+}
+
+// ----------------------------------------------------------------------------------------
+
+std::vector<detection> make_random_detections(unsigned long num_dets)
+{
+    std::vector<detection> dets(num_dets);
+    for (unsigned long i = 0; i < dets.size(); ++i)
+    {
+        dets[i].measurements = object_properties[i] + randm(num_properties,1,rnd)*0.1;
+    }
+    return dets;
+}
+
+// ----------------------------------------------------------------------------------------
+
+int main()
+{
+    initialize_object_properties();
+
+
+    std::vector<track_history> data;
+    data.push_back(make_random_tracking_data_for_training());
+    data.push_back(make_random_tracking_data_for_training());
+    data.push_back(make_random_tracking_data_for_training());
+    data.push_back(make_random_tracking_data_for_training());
+    data.push_back(make_random_tracking_data_for_training());
+
+
+    structural_track_association_trainer trainer;
+    trainer.be_verbose();
+    trainer.set_c(100);
+
+    track_association_function<detection> assoc = trainer.train(data);
+
+    cout << "accuracy on training data: "<< test_track_association_function(assoc, data) << endl;
+    cout << "cross validation: "<< cross_validate_track_association_trainer(trainer, data, 5) << endl;
+
+    std::vector<detection> dets;
+    std::vector<track> tracks;
+
+    cout << "number of tracks: "<< tracks.size() << endl;
+    dets = make_random_detections(3);
+    assoc(tracks, dets);
+    cout << "number of tracks: "<< tracks.size() << endl;
+
+    dets = make_random_detections(3);
+    assoc(tracks, dets);
+    cout << "number of tracks: "<< tracks.size() << endl;
+
+    dets = make_random_detections(4);
+    assoc(tracks, dets);
+    cout << "number of tracks: "<< tracks.size() << endl;
+
+    dets = make_random_detections(3);
+    assoc(tracks, dets);
+    cout << "number of tracks: "<< tracks.size() << endl;
+    for (unsigned long i = 0; i < tracks.size(); ++i)
+        cout << "   time since last association: "<< tracks[i].time_since_last_association << endl;
+
+    dets = make_random_detections(3);
+    assoc(tracks, dets);
+    cout << "number of tracks: "<< tracks.size() << endl;
+    for (unsigned long i = 0; i < tracks.size(); ++i)
+        cout << "   time since last association: "<< tracks[i].time_since_last_association << endl;
+
+
+    ofstream fout("track_assoc.svm", ios::binary);
+    serialize(assoc, fout);
+    fout.close();
+
+    ifstream fin("track_assoc.svm", ios::binary);
+    deserialize(assoc, fin);
+}
+
+// ----------------------------------------------------------------------------------------
+