AGAINST--ByteTrack
Paper address: link
Code address: link
a background
The modified algorithm comes from ByteDance Lab. Its core purpose is to solve SORTthe defects in the test. Its basic idea is to separately process the high-score targets and low-score targets in the detection, and perform multi-level matching to achieve better results. Assign and track performance.
2. Algorithm principle
The main function of byte is to solve the problem of inaccurate matching of low detection scores and temporary occlusion of objects. The basic algorithm principle is:
- Target detection, for video data, there are several algorithms and methods for target detection
n, and its output structure can be constructedrect + centroid + score + labelin the form of ; - Divide the detection results according to , and divide the detection results into two parts
scoreand through threshold judgment ;high scorelow score - For the first match,
high scorematch the detection results in with the original trajectory (SORTsimilar to ), and some trajectories will not be successfully matched; - Match again, matching the remaining trajectories in step 3 with
low scorethe data in ; - For the detection frame that does not match the tracking track and has a high enough score, create a new tracking track. For the tracking track that does not match the detection frame, keep
mthe frame, and then match it when it appears again;
the pseudo code of the algorithm is as follows (from the paper):
3. Code Analysis
The core code is as follows, and its file name is:byte_tracker.py
def update(self, output_results, img_info, img_size):
self.frame_id += 1
activated_starcks = []
refind_stracks = []
lost_stracks = []
removed_stracks = []
if output_results.shape[1] == 5:
scores = output_results[:, 4]
bboxes = output_results[:, :4]
else:
output_results = output_results.cpu().numpy()
scores = output_results[:, 4] * output_results[:, 5]
bboxes = output_results[:, :4] # x1y1x2y2
img_h, img_w = img_info[0], img_info[1]
scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))
bboxes /= scale
remain_inds = scores > self.args.track_thresh
inds_low = scores > 0.1
inds_high = scores < self.args.track_thresh
inds_second = np.logical_and(inds_low, inds_high)
dets_second = bboxes[inds_second]
dets = bboxes[remain_inds]
scores_keep = scores[remain_inds]
scores_second = scores[inds_second]
if len(dets) > 0:
'''Detections'''
detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
(tlbr, s) in zip(dets, scores_keep)]
else:
detections = []
''' Add newly detected tracklets to tracked_stracks'''
unconfirmed = []
tracked_stracks = [] # type: list[STrack]
for track in self.tracked_stracks:
if not track.is_activated:
unconfirmed.append(track)
else:
tracked_stracks.append(track)
''' Step 2: First association, with high score detection boxes'''
strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
# Predict the current location with KF
STrack.multi_predict(strack_pool)
dists = matching.iou_distance(strack_pool, detections)
if not self.args.mot20:
dists = matching.fuse_score(dists, detections)
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh)
for itracked, idet in matches:
track = strack_pool[itracked]
det = detections[idet]
if track.state == TrackState.Tracked:
track.update(detections[idet], self.frame_id)
activated_starcks.append(track)
else:
track.re_activate(det, self.frame_id, new_id=False)
refind_stracks.append(track)
''' Step 3: Second association, with low score detection boxes'''
# association the untrack to the low score detections
if len(dets_second) > 0:
'''Detections'''
detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
(tlbr, s) in zip(dets_second, scores_second)]
else:
detections_second = []
r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
dists = matching.iou_distance(r_tracked_stracks, detections_second)
matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)
for itracked, idet in matches:
track = r_tracked_stracks[itracked]
det = detections_second[idet]
if track.state == TrackState.Tracked:
track.update(det, self.frame_id)
activated_starcks.append(track)
else:
track.re_activate(det, self.frame_id, new_id=False)
refind_stracks.append(track)
for it in u_track:
track = r_tracked_stracks[it]
if not track.state == TrackState.Lost:
track.mark_lost()
lost_stracks.append(track)
'''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
detections = [detections[i] for i in u_detection]
dists = matching.iou_distance(unconfirmed, detections)
if not self.args.mot20:
dists = matching.fuse_score(dists, detections)
matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
for itracked, idet in matches:
unconfirmed[itracked].update(detections[idet], self.frame_id)
activated_starcks.append(unconfirmed[itracked])
for it in u_unconfirmed:
track = unconfirmed[it]
track.mark_removed()
removed_stracks.append(track)
""" Step 4: Init new stracks"""
for inew in u_detection:
track = detections[inew]
if track.score < self.det_thresh:
continue
track.activate(self.kalman_filter, self.frame_id)
activated_starcks.append(track)
""" Step 5: Update state"""
for track in self.lost_stracks:
if self.frame_id - track.end_frame > self.max_time_lost:
track.mark_removed()
removed_stracks.append(track)
# print('Ramained match {} s'.format(t4-t3))
self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
self.lost_stracks.extend(lost_stracks)
self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
self.removed_stracks.extend(removed_stracks)
self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
# get scores of lost tracks
output_stracks = [track for track in self.tracked_stracks if track.is_activated]
return output_stracks