论文名称：ByteTrack: Multi-Object Tracking by Associating Every Detection Box
MOT排行榜: MOT17，MOT20 private赛道No1. Online
论文代码： https://github.com/ifzhang/ByteTrack
发布时间：2021
这篇论文原作者在知乎有文章
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
然后就是另一篇概述的很好的文章
目标跟踪之 MOT 经典算法：ByteTrack 算法原理以及多类别跟踪
这篇文章主要就是把低置信度的检测框也参与匹配，与DeepSORT都是为了减少missing Track的情况。
DeepSORT主要是将长时间未匹配到的跟踪预测框与检测框匹配的权重降低，优先匹配missing track时间短的跟踪预测框。
ByteTrack主要是加入了低置信度的检测框与轨迹进行匹配，重点提升了被遮挡目标仍能保持匹配。
DeepSORT的级联匹配与ByteTrack的匹配
DeepSORT
优先匹配time_since_update小的tracks
完整代码见：https://github1s.com/nwojke/deep_sort/blob/HEAD/deep_sort/linear_assignment.py
下面贴一下关键代码部分：
Tip：匹配函数的参数见下方折叠代码
点击查看代码
def matching_cascade(
distance_metric, max_distance, cascade_depth, tracks, detections,
track_indices=None, detection_indices=None):
"""Run matching cascade.
Parameters
----------
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as well as
a list of N track indices and M detection indices. The metric should
return the NxM dimensional cost matrix, where element (i, j) is the
association cost between the i-th track in the given track indices and
the j-th detection in the given detection indices.
max_distance : float
Gating threshold. Associations with cost larger than this value are
disregarded.
cascade_depth: int
The cascade depth, should be se to the maximum track age.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : Optional[List[int]]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above). Defaults to all tracks.
detection_indices : Optional[List[int]]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above). Defaults to all
detections.
Returns
-------
(List[(int, int)], List[int], List[int])
Returns a tuple with the following three entries:
* A list of matched track and detection indices.
* A list of unmatched track indices.
* A list of unmatched detection indices.
"""
# ...
def matching_cascade(distance_metric, max_distance, cascade_depth,
tracks, detections,track_indices=None, detection_indices=None):
if track_indices is None:
track_indices = list(range(len(tracks)))
if detection_indices is None:
detection_indices = list(range(len(detections)))
# init
unmatched_detections = detection_indices
matches = []
# cascade_depth 就是track保留的最大值 Age maximum
for level in range(cascade_depth):
if len(unmatched_detections) == 0: # No detections left
break
track_indices_l = [
k for k in track_indices
# time_since_update 是上次update的次数，优先匹配time_since_update小的tracks
if tracks[k].time_since_update == 1 + level
]
if len(track_indices_l) == 0: # Nothing to match at this level
continue
matches_l, _, unmatched_detections = min_cost_matching(distance_metric,
max_distance, tracks, detections, track_indices_l, unmatched_detections)
matches += matches_l
unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
return matches, unmatched_tracks, unmatched_detections
ByteTrack
Step 1: Add newly detected tracklets to tracked_stracks
Step 2: First association, with high score detection boxes ☆☆☆☆☆
Step 3: Second association, with low score detection boxes **☆☆☆☆☆ **
Step 4: Init new stracks
Step 5: Update state
完整代码见：https://github1s.com/ifzhang/ByteTrack/blob/HEAD/yolox/tracker/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] # ☆☆☆☆☆ 第二批置信度低的检测框
dets = bboxes[remain_inds] # ☆☆☆☆☆ 第一批置信度高的检测框
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))
# ...
return output_stracks