/**
* Combines hypotheses from tracking and detection.
*
* @param trackingWorked If the sequential tracker updated the track region successfully or not
* @return true a hypothesis was found, false if it failed to find a hypothesis
*/
protected boolean hypothesisFusion(boolean trackingWorked, boolean detectionWorked) {
valid = false;
boolean uniqueDetection = detectionWorked && !detection.isAmbiguous();
TldRegion detectedRegion = detection.getBest();
double confidenceTarget;
if (trackingWorked) {
// get the scores from tracking and detection
double scoreTrack = template.computeConfidence(trackerRegion_I32);
double scoreDetected = 0;
if (uniqueDetection) {
scoreDetected = detectedRegion.confidence;
}
double adjustment = strongMatch ? 0.07 : 0.02;
if (uniqueDetection && scoreDetected > scoreTrack + adjustment) {
// if there is a unique detection and it has higher confidence than the
// track region, use the detected region
TldHelperFunctions.convertRegion(detectedRegion.rect, targetRegion);
confidenceTarget = detectedRegion.confidence;
// if it's far away from the current track, re-evaluate if it's a strongMatch
checkNewTrackStrong(scoreDetected);
} else {
// Otherwise use the tracker region
targetRegion.set(trackerRegion);
confidenceTarget = scoreTrack;
strongMatch |= confidenceTarget > config.confidenceThresholdStrong;
// see if the most likely detected region overlaps the track region
if (strongMatch && confidenceTarget >= config.confidenceThresholdLower) {
valid = true;
}
}
} else if (uniqueDetection) {
// just go with the best detected region
detectedRegion = detection.getBest();
TldHelperFunctions.convertRegion(detectedRegion.rect, targetRegion);
confidenceTarget = detectedRegion.confidence;
strongMatch = confidenceTarget > config.confidenceThresholdStrong;
} else {
return false;
}
return confidenceTarget >= config.confidenceAccept;
}
/**
* Updates track region.
*
* @param image Next image in the sequence.
* @return true if the object could be found and false if not
*/
public boolean track(T image) {
boolean success = true;
valid = false;
imagePyramid.process(image);
template.setImage(image);
variance.setImage(image);
fern.setImage(image);
if (reacquiring) {
// It can reinitialize if there is a single detection
detection.detectionCascade(cascadeRegions);
if (detection.isSuccess() && !detection.isAmbiguous()) {
TldRegion region = detection.getBest();
reacquiring = false;
valid = false;
// set it to the detected region
ImageRectangle r = region.rect;
targetRegion.set(r.x0, r.y0, r.x1, r.y1);
// get tracking running again
tracking.initialize(imagePyramid);
checkNewTrackStrong(region.confidence);
} else {
success = false;
}
} else {
detection.detectionCascade(cascadeRegions);
// update the previous track region using the tracker
trackerRegion.set(targetRegion);
boolean trackingWorked = tracking.process(imagePyramid, trackerRegion);
trackingWorked &= adjustRegion.process(tracking.getPairs(), trackerRegion);
TldHelperFunctions.convertRegion(trackerRegion, trackerRegion_I32);
if (hypothesisFusion(trackingWorked, detection.isSuccess())) {
// if it found a hypothesis and it is valid for learning, then learn
if (valid && performLearning) {
learning.updateLearning(targetRegion);
}
} else {
reacquiring = true;
success = false;
}
}
if (strongMatch) {
previousTrackArea = targetRegion.area();
}
return success;
}
