Tracking objects when there are no objects detected #22
Description
Hello while using the SORT object from sort_tracker.py I realized that, after I stop detecting objects, if I send empty boxes, confidence and classes to the update function I still get 1 or more objects as being tracked. I thought the max_lost parameter would prevent this? the paper indicates that the tracks should be terminated if they are not detected for Tlost frames, so shouln't an empty detection count as all tracks not being detected in that frame?
Adding something like this should take care of the tracks if no object is detected in a frame:
for t in unmatched_tracks:
track_id = track_ids[t]
bbox = bbox_tracks[t, :]
confidence = self.tracks[track_id].detection_confidence
cid = self.tracks[track_id].class_id
self._update_track(track_id, self.frame_count, bbox, detection_confidence=confidence, class_id=cid, lost=1)
if self.tracks[track_id].lost > self.max_lost:
self._remove_track(track_id)
# In case there are no detections, update tracks with the current prediction, if lost > max_lost remove track.
if len(bboxes) == 0:
for i in range(len(bbox_tracks)):
track_id = track_ids[i]
bbox = bbox_tracks[i, :]
confidence = self.tracks[track_id].detection_confidence
cid = self.tracks[track_id].class_id
self._update_track(track_id, self.frame_count, bbox, detection_confidence=confidence, class_id=cid, lost=1)
if self.tracks[track_id].lost > self.max_lost:
self._remove_track(track_id)Also talking about the parameter "lost" for the track class isn't that line odd? if you set max_lost to let's say 5, then the condition lost > max_lost cannot be reached since the lost parameter is always reset to 1. In my case I commented that line out to make it work.
Here's a little demo that I wrote, I recommend runing it (without the changes I made) with a video where after the tracked object leaves the screen there are no more objects to detect, to see what I mean.
...
detector = Yolov4Detector()
tracker = CentroidTracker()
for i in range(len(images)):
detection_bboxes = []
detection_confidences = []
detection_class_ids = []
im = Image.open(images[i])
transform = detector.get_transform()
new_im = transform(im)
new_im.unsqueeze_(0)
dets = detector.detect(new_im)[0]
for det in dets:
xmin = int(det['bbox']['x1'] * im.size[0])
ymin = int(det['bbox']['y1'] * im.size[1])
xmax = int(det['bbox']['x2'] * im.size[0])
ymax = int(det['bbox']['y2'] * im.size[1])
width = abs(xmin - xmax)
height = abs(ymin - ymax)
detection_bboxes.append([xmin, xmax, width, height])
detection_confidences.append(det['confidence'])
detection_class_ids.append(0) #There's only one class
detection_bboxes = np.array(detection_bboxes)
detection_confidences = np.array(detection_confidences)
detection_class_ids = np.array(detection_class_ids)
output_tracks = tracker.update(detection_bboxes, detection_confidences, detection_class_ids)
for track in output_tracks:
frame, id, bb_left, bb_top, bb_width, bb_height, confidence, x, y, z = track
assert len(track) == 10
print(track)Looks like someone else had the same issue #21 (comment)