Skip to content

ByteTrack

Initialize the ByteTrack object.

Parameters:

Name Type Description Default

track_activation_threshold

float

Detection confidence threshold for track activation. Increasing track_activation_threshold improves accuracy and stability but might miss true detections. Decreasing it increases completeness but risks introducing noise and instability.

 0.25

lost_track_buffer

int

Number of frames to buffer when a track is lost. Increasing lost_track_buffer enhances occlusion handling, significantly reducing the likelihood of track fragmentation or disappearance caused by brief detection gaps.

 30

minimum_matching_threshold

float

Threshold for matching tracks with detections. Increasing minimum_matching_threshold improves accuracy but risks fragmentation. Decreasing it improves completeness but risks false positives and drift.

 0.8

frame_rate

int

The frame rate of the video.

 30

minimum_consecutive_frames

int

Number of consecutive frames that an object must be tracked before it is considered a 'valid' track. Increasing minimum_consecutive_frames prevents the creation of accidental tracks from false detection or double detection, but risks missing shorter tracks.

 1
Source code in supervision/tracker/byte_tracker/core.py 
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
class ByteTrack:
    """
    Initialize the ByteTrack object.

    <video controls>
        <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-traces.mp4" type="video/mp4">
    </video>

    Parameters:
        track_activation_threshold (float): Detection confidence threshold
            for track activation. Increasing track_activation_threshold improves accuracy
            and stability but might miss true detections. Decreasing it increases
            completeness but risks introducing noise and instability.
        lost_track_buffer (int): Number of frames to buffer when a track is lost.
            Increasing lost_track_buffer enhances occlusion handling, significantly
            reducing the likelihood of track fragmentation or disappearance caused
            by brief detection gaps.
        minimum_matching_threshold (float): Threshold for matching tracks with detections.
            Increasing minimum_matching_threshold improves accuracy but risks fragmentation.
            Decreasing it improves completeness but risks false positives and drift.
        frame_rate (int): The frame rate of the video.
        minimum_consecutive_frames (int): Number of consecutive frames that an object must
            be tracked before it is considered a 'valid' track.
            Increasing minimum_consecutive_frames prevents the creation of accidental tracks from
            false detection or double detection, but risks missing shorter tracks.
    """  # noqa: E501 // docs

    def __init__(
        self,
        track_activation_threshold: float = 0.25,
        lost_track_buffer: int = 30,
        minimum_matching_threshold: float = 0.8,
        frame_rate: int = 30,
        minimum_consecutive_frames: int = 1,
    ):
        self.track_activation_threshold = track_activation_threshold
        self.minimum_matching_threshold = minimum_matching_threshold

        self.frame_id = 0
        self.det_thresh = self.track_activation_threshold + 0.1
        self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
        self.minimum_consecutive_frames = minimum_consecutive_frames
        self.kalman_filter = KalmanFilter()
        self.shared_kalman = KalmanFilter()

        self.tracked_tracks: List[STrack] = []
        self.lost_tracks: List[STrack] = []
        self.removed_tracks: List[STrack] = []

        # Warning, possible bug: If you also set internal_id to start at 1,
        # all traces will be connected across objects.
        self.internal_id_counter = IdCounter()
        self.external_id_counter = IdCounter(start_id=1)

    def update_with_detections(self, detections: Detections) -> Detections:
        """
        Updates the tracker with the provided detections and returns the updated
        detection results.

        Args:
            detections (Detections): The detections to pass through the tracker.

        Example:
            ```python
            import supervision as sv
            from ultralytics import YOLO

            model = YOLO(<MODEL_PATH>)
            tracker = sv.ByteTrack()

            box_annotator = sv.BoxAnnotator()
            label_annotator = sv.LabelAnnotator()

            def callback(frame: np.ndarray, index: int) -> np.ndarray:
                results = model(frame)[0]
                detections = sv.Detections.from_ultralytics(results)
                detections = tracker.update_with_detections(detections)

                labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

                annotated_frame = box_annotator.annotate(
                    scene=frame.copy(), detections=detections)
                annotated_frame = label_annotator.annotate(
                    scene=annotated_frame, detections=detections, labels=labels)
                return annotated_frame

            sv.process_video(
                source_path=<SOURCE_VIDEO_PATH>,
                target_path=<TARGET_VIDEO_PATH>,
                callback=callback
            )
            ```
        """
        tensors = np.hstack(
            (
                detections.xyxy,
                detections.confidence[:, np.newaxis],
            )
        )
        tracks = self.update_with_tensors(tensors=tensors)

        if len(tracks) > 0:
            detection_bounding_boxes = np.asarray([det[:4] for det in tensors])
            track_bounding_boxes = np.asarray([track.tlbr for track in tracks])

            ious = box_iou_batch(detection_bounding_boxes, track_bounding_boxes)

            iou_costs = 1 - ious

            matches, _, _ = matching.linear_assignment(iou_costs, 0.5)
            detections.tracker_id = np.full(len(detections), -1, dtype=int)
            for i_detection, i_track in matches:
                detections.tracker_id[i_detection] = int(
                    tracks[i_track].external_track_id
                )

            return detections[detections.tracker_id != -1]

        else:
            detections = Detections.empty()
            detections.tracker_id = np.array([], dtype=int)

            return detections

    def reset(self) -> None:
        """
        Resets the internal state of the ByteTrack tracker.

        This method clears the tracking data, including tracked, lost,
        and removed tracks, as well as resetting the frame counter. It's
        particularly useful when processing multiple videos sequentially,
        ensuring the tracker starts with a clean state for each new video.
        """
        self.frame_id = 0
        self.internal_id_counter.reset()
        self.external_id_counter.reset()
        self.tracked_tracks = []
        self.lost_tracks = []
        self.removed_tracks = []

    def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
        """
        Updates the tracker with the provided tensors and returns the updated tracks.

        Parameters:
            tensors: The new tensors to update with.

        Returns:
            List[STrack]: Updated tracks.
        """
        self.frame_id += 1
        activated_starcks = []
        refind_stracks = []
        lost_stracks = []
        removed_stracks = []

        scores = tensors[:, 4]
        bboxes = tensors[:, :4]

        remain_inds = scores > self.track_activation_threshold
        inds_low = scores > 0.1
        inds_high = scores < self.track_activation_threshold

        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),
                    score_keep,
                    self.minimum_consecutive_frames,
                    self.shared_kalman,
                    self.internal_id_counter,
                    self.external_id_counter,
                )
                for (tlbr, score_keep) 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_tracks:
            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_tracks(tracked_stracks, self.lost_tracks)
        # Predict the current location with KF
        STrack.multi_predict(strack_pool, self.shared_kalman)
        dists = matching.iou_distance(strack_pool, detections)

        dists = matching.fuse_score(dists, detections)
        matches, u_track, u_detection = matching.linear_assignment(
            dists, thresh=self.minimum_matching_threshold
        )

        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)
                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),
                    score_second,
                    self.minimum_consecutive_frames,
                    self.shared_kalman,
                    self.internal_id_counter,
                    self.external_id_counter,
                )
                for (tlbr, score_second) 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)
                refind_stracks.append(track)

        for it in u_track:
            track = r_tracked_stracks[it]
            if not track.state == TrackState.Lost:
                track.state = TrackState.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)

        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.state = TrackState.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_tracks:
            if self.frame_id - track.frame_id > self.max_time_lost:
                track.state = TrackState.Removed
                removed_stracks.append(track)

        self.tracked_tracks = [
            t for t in self.tracked_tracks if t.state == TrackState.Tracked
        ]
        self.tracked_tracks = joint_tracks(self.tracked_tracks, activated_starcks)
        self.tracked_tracks = joint_tracks(self.tracked_tracks, refind_stracks)
        self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks)
        self.lost_tracks.extend(lost_stracks)
        self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks)
        self.removed_tracks = removed_stracks
        self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
            self.tracked_tracks, self.lost_tracks
        )
        output_stracks = [track for track in self.tracked_tracks if track.is_activated]

        return output_stracks

Functions

reset()

Resets the internal state of the ByteTrack tracker.

This method clears the tracking data, including tracked, lost, and removed tracks, as well as resetting the frame counter. It's particularly useful when processing multiple videos sequentially, ensuring the tracker starts with a clean state for each new video.

Source code in supervision/tracker/byte_tracker/core.py 
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
def reset(self) -> None:
    """
    Resets the internal state of the ByteTrack tracker.

    This method clears the tracking data, including tracked, lost,
    and removed tracks, as well as resetting the frame counter. It's
    particularly useful when processing multiple videos sequentially,
    ensuring the tracker starts with a clean state for each new video.
    """
    self.frame_id = 0
    self.internal_id_counter.reset()
    self.external_id_counter.reset()
    self.tracked_tracks = []
    self.lost_tracks = []
    self.removed_tracks = []

update_with_detections(detections)

Updates the tracker with the provided detections and returns the updated detection results.

Parameters:

Name Type Description Default

detections

Detections

The detections to pass through the tracker.

 required
Example 
import supervision as sv
from ultralytics import YOLO

model = YOLO(<MODEL_PATH>)
tracker = sv.ByteTrack()

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

def callback(frame: np.ndarray, index: int) -> np.ndarray:
    results = model(frame)[0]
    detections = sv.Detections.from_ultralytics(results)
    detections = tracker.update_with_detections(detections)

    labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

    annotated_frame = box_annotator.annotate(
        scene=frame.copy(), detections=detections)
    annotated_frame = label_annotator.annotate(
        scene=annotated_frame, detections=detections, labels=labels)
    return annotated_frame

sv.process_video(
    source_path=<SOURCE_VIDEO_PATH>,
    target_path=<TARGET_VIDEO_PATH>,
    callback=callback
)
Source code in supervision/tracker/byte_tracker/core.py 
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
def update_with_detections(self, detections: Detections) -> Detections:
    """
    Updates the tracker with the provided detections and returns the updated
    detection results.

    Args:
        detections (Detections): The detections to pass through the tracker.

    Example:
        ```python
        import supervision as sv
        from ultralytics import YOLO

        model = YOLO(<MODEL_PATH>)
        tracker = sv.ByteTrack()

        box_annotator = sv.BoxAnnotator()
        label_annotator = sv.LabelAnnotator()

        def callback(frame: np.ndarray, index: int) -> np.ndarray:
            results = model(frame)[0]
            detections = sv.Detections.from_ultralytics(results)
            detections = tracker.update_with_detections(detections)

            labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

            annotated_frame = box_annotator.annotate(
                scene=frame.copy(), detections=detections)
            annotated_frame = label_annotator.annotate(
                scene=annotated_frame, detections=detections, labels=labels)
            return annotated_frame

        sv.process_video(
            source_path=<SOURCE_VIDEO_PATH>,
            target_path=<TARGET_VIDEO_PATH>,
            callback=callback
        )
        ```
    """
    tensors = np.hstack(
        (
            detections.xyxy,
            detections.confidence[:, np.newaxis],
        )
    )
    tracks = self.update_with_tensors(tensors=tensors)

    if len(tracks) > 0:
        detection_bounding_boxes = np.asarray([det[:4] for det in tensors])
        track_bounding_boxes = np.asarray([track.tlbr for track in tracks])

        ious = box_iou_batch(detection_bounding_boxes, track_bounding_boxes)

        iou_costs = 1 - ious

        matches, _, _ = matching.linear_assignment(iou_costs, 0.5)
        detections.tracker_id = np.full(len(detections), -1, dtype=int)
        for i_detection, i_track in matches:
            detections.tracker_id[i_detection] = int(
                tracks[i_track].external_track_id
            )

        return detections[detections.tracker_id != -1]

    else:
        detections = Detections.empty()
        detections.tracker_id = np.array([], dtype=int)

        return detections

update_with_tensors(tensors)

Updates the tracker with the provided tensors and returns the updated tracks.

Parameters:

Name Type Description Default

tensors

ndarray

The new tensors to update with.

 required

Returns:

Type Description
List[STrack]

List[STrack]: Updated tracks.
Source code in supervision/tracker/byte_tracker/core.py 
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
    """
    Updates the tracker with the provided tensors and returns the updated tracks.

    Parameters:
        tensors: The new tensors to update with.

    Returns:
        List[STrack]: Updated tracks.
    """
    self.frame_id += 1
    activated_starcks = []
    refind_stracks = []
    lost_stracks = []
    removed_stracks = []

    scores = tensors[:, 4]
    bboxes = tensors[:, :4]

    remain_inds = scores > self.track_activation_threshold
    inds_low = scores > 0.1
    inds_high = scores < self.track_activation_threshold

    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),
                score_keep,
                self.minimum_consecutive_frames,
                self.shared_kalman,
                self.internal_id_counter,
                self.external_id_counter,
            )
            for (tlbr, score_keep) 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_tracks:
        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_tracks(tracked_stracks, self.lost_tracks)
    # Predict the current location with KF
    STrack.multi_predict(strack_pool, self.shared_kalman)
    dists = matching.iou_distance(strack_pool, detections)

    dists = matching.fuse_score(dists, detections)
    matches, u_track, u_detection = matching.linear_assignment(
        dists, thresh=self.minimum_matching_threshold
    )

    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)
            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),
                score_second,
                self.minimum_consecutive_frames,
                self.shared_kalman,
                self.internal_id_counter,
                self.external_id_counter,
            )
            for (tlbr, score_second) 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)
            refind_stracks.append(track)

    for it in u_track:
        track = r_tracked_stracks[it]
        if not track.state == TrackState.Lost:
            track.state = TrackState.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)

    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.state = TrackState.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_tracks:
        if self.frame_id - track.frame_id > self.max_time_lost:
            track.state = TrackState.Removed
            removed_stracks.append(track)

    self.tracked_tracks = [
        t for t in self.tracked_tracks if t.state == TrackState.Tracked
    ]
    self.tracked_tracks = joint_tracks(self.tracked_tracks, activated_starcks)
    self.tracked_tracks = joint_tracks(self.tracked_tracks, refind_stracks)
    self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks)
    self.lost_tracks.extend(lost_stracks)
    self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks)
    self.removed_tracks = removed_stracks
    self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
        self.tracked_tracks, self.lost_tracks
    )
    output_stracks = [track for track in self.tracked_tracks if track.is_activated]

    return output_stracks

Comments