//////////////////////////////////////////////////////////////////////
-scalar_t detection_score(int true_label, scalar_t flip_noise) {
- if((true_label > 0) == (drand48() < flip_noise)) {
- return 1.0 + 0.2 * (drand48() - 0.5);
+scalar_t detection_score(scalar_t a, scalar_t b, scalar_t score_noise, scalar_t flip_noise) {
+ if(drand48() > flip_noise) {
+ return a + score_noise * (2.0 * drand48() - 1.0);
} else {
- return - 1.0 + 0.2 * (drand48() - 0.5);
+ return b + score_noise * (2.0 * drand48() - 1.0);
}
}
int main(int argc, char **argv) {
- int nb_locations = 6;
- int nb_time_steps = 5;
+ int nb_locations = 7;
+ int nb_time_steps = 8;
int motion_amplitude = 1;
Tracker *tracker = new Tracker(nb_time_steps, nb_locations);
+ // We define the spatial structures by stating what are the possible
+ // motions of targets, and what are the entrances and the
+ // exits.
+
+ // Here our example is a 1D space with motions from any location to
+ // any location less than motion_amplitude away, entrance at
+ // location 0 and exit at location nb_locations-1.
+
for(int l = 0; l < nb_locations; l++) {
for(int k = 0; k < nb_locations; k++) {
tracker->allowed_motion[l][k] = abs(l - k) <= motion_amplitude;
tracker->exits[nb_locations - 1] = 1;
}
+ // We construct the graph corresponding to this structure
+
tracker->build_graph();
- // We generate synthetic detection scores at location
- // nb_locations/2, with 5% false detection (FP or FN)
+ // Then, we specify for every location and time step what is the
+ // detection score there.
+
+ scalar_t flip_noise = 0.05;
+ scalar_t score_noise = 0.0;
+
+ // We first put a background noise, with negative scores at every
+ // location.
for(int t = 0; t < nb_time_steps; t++) {
for(int l = 0; l < nb_locations; l++) {
- tracker->detection_score[t][l] = detection_score(-1, 0.95);
+ tracker->detection_scores[t][l] = detection_score(-1.0, 1.0, score_noise, flip_noise);
+ }
+ }
+
+ // Then we two targets with the typical local minimum:
+ //
+ // * Target A moves from location 0 to the middle, stays there for a
+ // while, and comes back, and is strongly detected on the first
+ // half
+ //
+ // * Target B moves from location nb_locations-1 to the middle, stay
+ // there for a while, and comes back, and is strongly detected on
+ // the second half
+
+ int la, lb; // Target locations
+ scalar_t sa, sb; // Target detection scores
+ for(int t = 0; t < nb_time_steps; t++) {
+ if(t < nb_time_steps/2) {
+ la = t;
+ lb = nb_locations - 1 - t;
+ sa = detection_score(10.0, -1.0, score_noise, flip_noise);
+ sb = detection_score( 1.0, -1.0, score_noise, flip_noise);
+ } else {
+ la = nb_time_steps - 1 - t;
+ lb = t - nb_time_steps + nb_locations;
+ sa = detection_score( 1.0, -1.0, score_noise, flip_noise);
+ sb = detection_score(10.0, -1.0, score_noise, flip_noise);
}
- tracker->detection_score[t][nb_locations/2] = detection_score(1, 0.95);
+
+ if(la > nb_locations/2 - 1) la = nb_locations/2 - 1;
+ if(lb < nb_locations/2 + 1) lb = nb_locations/2 + 1;
+
+ tracker->detection_scores[t][la] = sa;
+ tracker->detection_scores[t][lb] = sb;
}
+ // Does the tracking per se
+
tracker->track();
+ // Prints the detected trajectories
+
for(int t = 0; t < tracker->nb_trajectories(); t++) {
cout << "TRAJECTORY "
<< t
- << " [starting " << tracker->trajectory_entrance_time(t) << "]";
+ << " [starting " << tracker->trajectory_entrance_time(t)
+ << ", score " << tracker->trajectory_score(t) << "]";
for(int u = 0; u < tracker->trajectory_duration(t); u++) {
cout << " " << tracker->trajectory_location(t, u);
}
cout << endl;
}
+ // Save the underlying graph in the dot format, with occupied edges
+ // marked in bold.
+
{
ofstream dot("graph.dot");
tracker->print_graph_dot(&dot);