def __assign_detections_to_tracks_munkres(self, detections, frame_id, save=False):
# if there are no tracks yet, all detections are new tracks
if len(self.tracks) == 0:
for det in detections:
t = Track()
t.add_to_track(det)
self.tracks.append(t)
return True
# find distance from all tracks to all detections and formulate dists matrix
dists = np.zeros(shape=(len(self.tracks), len(detections)))
for i, track in enumerate(self.tracks):
predicted_next_bb = track.get_predicted_next_bb()
for j, det in enumerate(detections):
dist = util.dist_btwn_bb_centroids(predicted_next_bb, det.bbox)
if track.is_singular():
max_dist = const.MAX_PIXELS_DIST_TRACK_START
else:
max_dist = const.MAX_PIXELS_DIST_TRACK
if dist > max_dist:
dist = 1e6 # set to arbitrarily high number
dists[i, j] = dist
# set all tracks as unassigned
for t in self.tracks:
t.has_match = False
# assign all detections to tracks with munkres algorithm
assigned_rows, assigned_cols = linear_sum_assignment(dists)
for idx, row in enumerate(assigned_rows):
col = assigned_cols[idx]
# if track is assigned a detection with dist=1e6, discard that assignment
if dists[row, col] != 1e6:
self.tracks[row].has_match = True
detections[col].has_match = True
self.tracks[row].add_to_track(detections[col])
self.tracks[row].num_misses = 0
# create new tracks from unassigned detections:
for det in detections:
if det.has_match is False:
t = Track()
t.add_to_track(det)
self.tracks.append(t)
# keep track of how many times a track has gone unassigned
for t in self.tracks:
if t.has_match is False:
t.num_misses += 1
# t.propagate_track(frame_id=frame_id)
# cleanup any duplicate tracks that have formed (TODO: how do they form?)
self.__delete_duplicate_tracks()
# save dead tracks before deletion
if save:
self.__save_tracks_to_json()
# remove dead tracks
self.tracks = [t for t in self.tracks if (t.is_dead() is False and t.delete_me is False)]
def _assign_detections_to_tracks(self, detections, frame_id, save=False):
# if there are no tracks yet, all detections are new tracks
if len(self.tracks) == 0:
for det in detections:
t = Track()
t.add_to_track(det)
self.tracks.append(t)
return True
# assign detections to existing tracks
for track in self.tracks:
track.has_match = False
predicted_next_bb = track.get_predicted_next_bb()
for det in detections:
# singular tracks search radially
if track.is_singular():
iou = util.bb_intersection_over_union(
predicted_next_bb, det.bbox)
dist = util.dist_btwn_bb_centroids(predicted_next_bb,
det.bbox)
if dist < const.MAX_PIXELS_DIST_TRACK_START and iou > const.MIN_IOU_TRACK_START:
track.add_to_track(det)
track.has_match = True
track.num_misses = 0
break
# established tracks search in predicted location
elif track.is_established():
# TODO: get distance, iou to det
iou = util.bb_intersection_over_union(
predicted_next_bb, det.bbox)
dist = util.dist_btwn_bb_centroids(predicted_next_bb,
det.bbox)
if dist < const.MAX_PIXELS_DIST_TRACK and iou > const.MIN_IOU_TRACK:
# print 'pred_n=', predicted_next_bb
# print 'iou={},dist={}'.format(iou, dist)
sim = self.face_rec_engine.compute_similarity(
det.fvec, track.get_latest_fvec())
# print 'sim={}'.format(sim)
track.add_to_track(det)
track.has_match = True
track.num_misses = 0
# TODO: handle case where decision is tough (2 detections very close)
break
# if no track was assigned, give penalty to track
if not track.has_match:
# delete singular tracks that didn't get assigned (probably false detection)
if track.num_misses > 0:
if track.is_singular():
track.delete_me = True
# print 'delete singular track'
else:
# TODO: continue track using predicted state
track.propagate_track(frame_id=frame_id)
track.num_misses += 1
else:
# reset match flag
track.has_match = False
for i, det in enumerate(detections):
# if det hasn't been assigned yet, create new tracks
if det.num_matches == 0:
# print 'new track created. len(tracks)={}, num_det={}'.format(len(tracks),len(detections))
t = Track()
t.add_to_track(det)
self.tracks.append(t)
elif det.num_matches > 1:
# print 'multiple assignment!! (num_matches({})={})'.format(i, det.num_matches)
pass
# TODO: resolve detections with multiple matches
# print 'num_tracks = {}, num dets = {}'.format(len(tracks), len(detections))
# cleanup any duplicate tracks that have formed (TODO: how do they form?)
self._delete_duplicate_tracks()
# save dead tracks before deletion
if save:
self._save_tracks_to_json()
# remove dead tracks
self.tracks = [
t for t in self.tracks
if (t.is_dead() is False and t.delete_me is False)
]
# for i, track in enumerate(tracks):
# print '{}: {}'.format(i, track.get_latest_bb())
return True
