Detection Smoother

supervision.detection.tools.smoother.DetectionsSmoother

A utility class for smoothing detections over multiple frames in video tracking. It maintains a history of detections for each track and provides smoothed predictions based on these histories.

Warning

• :class:~supervision.detection.tools.smoother.DetectionsSmoother requires the tracker_id for each detection. Refer to Roboflow Trackers for information on integrating tracking into your inference pipeline.
• This class is not compatible with segmentation models.
• When detections in a frame disagree on confidence presence — some tracks carry confidence scores and others do not — confidence is set to None for all smoothed detections in that frame.

Example

>>> import numpy as np
>>> import supervision as sv
>>> smoother = sv.DetectionsSmoother(length=3)
>>> detections_1 = sv.Detections(
...     xyxy=np.array([[0, 0, 10, 10]]),
...     confidence=np.array([0.5]),
...     tracker_id=np.array([1])
... )
>>> detections_2 = sv.Detections(
...     xyxy=np.array([[2, 2, 12, 12]]),
...     confidence=np.array([0.7]),
...     tracker_id=np.array([1])
... )
>>> smoothed = smoother.update_with_detections(detections_1)
>>> smoothed.xyxy
array([[ 0.,  0., 10., 10.]])
>>> smoothed = smoother.update_with_detections(detections_2)
>>> smoothed.xyxy
array([[ 1.,  1., 11., 11.]])
>>> smoothed.confidence
array([0.6])

import supervision as sv

from rfdetr import RFDETRMedium
from trackers import ByteTrackTracker

video_info = sv.VideoInfo.from_video_path(video_path="<SOURCE_FILE_PATH>")
frame_generator = sv.get_video_frames_generator(
    source_path="<SOURCE_FILE_PATH>")

model = RFDETRMedium()
tracker = ByteTrackTracker(
    frame_rate=video_info.fps,
    track_activation_threshold=0.25,
    minimum_consecutive_frames=1,
)
smoother = sv.DetectionsSmoother()

box_annotator = sv.BoxAnnotator()

with sv.VideoSink("<TARGET_FILE_PATH>", video_info=video_info) as sink:
    for frame in frame_generator:
        detections = model.predict(frame[:, :, ::-1])
        detections = tracker.update(detections)
        detections = detections[detections.tracker_id != -1]
        detections = smoother.update_with_detections(detections)

        annotated_frame = box_annotator.annotate(frame.copy(), detections)
        sink.write_frame(annotated_frame)

Source code in src/supervision/detection/tools/smoother.py

class DetectionsSmoother:
    """A utility class for smoothing detections over multiple frames in video tracking.
    It maintains a history of detections for each track and provides smoothed
    predictions based on these histories.

    <video controls>
        <source
            src="https://media.roboflow.com/supervision-detection-smoothing.mp4"
            type="video/mp4">
    </video>

    !!! warning

        - :class:`~supervision.detection.tools.smoother.DetectionsSmoother`
          requires the `tracker_id` for each detection. Refer to
          [Roboflow Trackers](https://trackers.roboflow.com/latest/) for
          information on integrating tracking into your inference pipeline.
        - This class is not compatible with segmentation models.
        - When detections in a frame disagree on confidence presence — some tracks
          carry confidence scores and others do not — `confidence` is set to `None`
          for all smoothed detections in that frame.

    Example:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> smoother = sv.DetectionsSmoother(length=3)
        >>> detections_1 = sv.Detections(
        ...     xyxy=np.array([[0, 0, 10, 10]]),
        ...     confidence=np.array([0.5]),
        ...     tracker_id=np.array([1])
        ... )
        >>> detections_2 = sv.Detections(
        ...     xyxy=np.array([[2, 2, 12, 12]]),
        ...     confidence=np.array([0.7]),
        ...     tracker_id=np.array([1])
        ... )
        >>> smoothed = smoother.update_with_detections(detections_1)
        >>> smoothed.xyxy
        array([[ 0.,  0., 10., 10.]])
        >>> smoothed = smoother.update_with_detections(detections_2)
        >>> smoothed.xyxy
        array([[ 1.,  1., 11., 11.]])
        >>> smoothed.confidence
        array([0.6])

        ```


        ```python
        import supervision as sv

        from rfdetr import RFDETRMedium
        from trackers import ByteTrackTracker

        video_info = sv.VideoInfo.from_video_path(video_path="<SOURCE_FILE_PATH>")
        frame_generator = sv.get_video_frames_generator(
            source_path="<SOURCE_FILE_PATH>")

        model = RFDETRMedium()
        tracker = ByteTrackTracker(
            frame_rate=video_info.fps,
            track_activation_threshold=0.25,
            minimum_consecutive_frames=1,
        )
        smoother = sv.DetectionsSmoother()

        box_annotator = sv.BoxAnnotator()

        with sv.VideoSink("<TARGET_FILE_PATH>", video_info=video_info) as sink:
            for frame in frame_generator:
                detections = model.predict(frame[:, :, ::-1])
                detections = tracker.update(detections)
                detections = detections[detections.tracker_id != -1]
                detections = smoother.update_with_detections(detections)

                annotated_frame = box_annotator.annotate(frame.copy(), detections)
                sink.write_frame(annotated_frame)
        ```
    """

    def __init__(self, length: int = 5) -> None:
        """
        Args:
            length: The maximum number of frames to consider for smoothing
                detections. Each update counts as one frame, including frames
                without tracker IDs. Defaults to 5.
        """
        self.tracks: defaultdict[int, deque[Detections | None]] = defaultdict(
            lambda: deque(maxlen=length)
        )

    def reset(self) -> None:
        """Clears the per-track detection history so the smoother can be reused across
        independent streams without carrying over frames from a previous stream. The
        configured window `length` is preserved.

        Examples:
            ```pycon
            >>> import numpy as np
            >>> import supervision as sv
            >>> smoother = sv.DetectionsSmoother()
            >>> detections = sv.Detections(
            ...     xyxy=np.array([[0, 0, 10, 10]]),
            ...     confidence=np.array([0.5]),
            ...     tracker_id=np.array([1])
            ... )
            >>> _ = smoother.update_with_detections(detections)
            >>> len(smoother.tracks)
            1
            >>> smoother.reset()
            >>> len(smoother.tracks)
            0

            ```
        """
        self.tracks.clear()

    def update_with_detections(self, detections: Detections) -> Detections:
        """Updates the smoother with a new set of detections from a frame.

        Every call counts as one frame toward the window `length`. Frames without
        tracker IDs, including empty ones, still age the cached history, but their
        detections are returned unchanged and each such frame emits a warning. The
        warning is raised before the history changes, so a warning turned into an
        error leaves the cached history intact.

        Args:
            detections: The detections to add to the smoother.

        Returns:
            Smoothed detections for the tracks present in this frame. When
            `detections` has no `tracker_id`, the input object itself is returned.
        """
        tracker_ids = detections.tracker_id
        if tracker_ids is None:
            warnings.warn(
                "Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
                "https://trackers.roboflow.com/latest/ for more "
                "information.",
                category=SupervisionWarnings,
            )
            self._age_tracks(active_ids=set())
            return detections

        for detection_idx in range(len(detections)):
            tracker_id = int(tracker_ids[detection_idx])
            self.tracks[tracker_id].append(detections.select(detection_idx))

        active_ids = set(tracker_ids.tolist())
        self._age_tracks(active_ids=active_ids)
        return self.get_smoothed_detections(track_ids=active_ids)

    def _age_tracks(self, active_ids: set[int]) -> None:
        """Advance every cached track by one frame and drop fully expired tracks.

        Args:
            active_ids: Track IDs that received a detection in the current frame.
        """
        # Tracks without a detection this frame get an empty sample, so the window
        # slides on every call and old samples fall out even while a track is absent.
        for track_id in self.tracks.keys():
            if track_id not in active_ids:
                self.tracks[track_id].append(None)

        for track_id in list(self.tracks.keys()):
            if all(d is None for d in self.tracks[track_id]):
                del self.tracks[track_id]

    def get_track(self, track_id: int) -> Detections | None:
        """Return the smoothed :class:`~supervision.detection.core.Detections` for a
        single track.

        Averages `xyxy` over all valid (non-`None`) frames in the track window.
        `confidence` is averaged only over frames that carry it; frames with
        `confidence=None` are excluded. When every valid frame carries compatible
        oriented-box corners, those corners are aligned and averaged too, and
        `xyxy` is derived from their resulting envelope. Mixed or incompatible
        oriented-box metadata is dropped to keep the returned geometry consistent.
        Every other field, such as `class_id`, `data` and `metadata`, is taken from
        the track's most recent frame.
        Returns `None` when the track is unknown or its entire window is empty.

        Args:
            track_id: The tracker ID whose smoothed detection to retrieve.

        Returns:
            Smoothed :class:`~supervision.detection.core.Detections` for the
            track, or `None` if the track is unknown or all frames in its window
            are empty.
        """
        track = self.tracks.get(track_id, None)
        if track is None:
            return None

        valid: list[Detections] = [d for d in track if d is not None]
        if len(valid) == 0:
            return None

        # Build on the newest frame: for a track active in the current frame that is
        # the current detection, so its class and data are up to date, and every
        # emitted track carries the same frame's metadata, which `Detections.merge`
        # requires to agree.
        ret = deepcopy(valid[-1])
        ret.xyxy = np.mean(np.stack([d.xyxy for d in valid], axis=0), axis=0)
        # Average confidence only over frames that carry it; frames with
        # confidence=None contribute nothing to the mean. Retain None when
        # no frame in the window carries confidence.
        confidences = [d.confidence for d in valid if d.confidence is not None]
        ret.confidence = np.mean(np.array(confidences), axis=0) if confidences else None

        corner_sets = [
            np.asarray(d.data[ORIENTED_BOX_COORDINATES])
            for d in valid
            if ORIENTED_BOX_COORDINATES in d.data
        ]
        if len(corner_sets) == len(valid) and len({c.shape for c in corner_sets}) == 1:
            reference = corner_sets[0]
            aligned = [
                _align_oriented_corners(corners, reference) for corners in corner_sets
            ]
            smoothed_corners = np.mean(np.stack(aligned, axis=0), axis=0)
            ret.data[ORIENTED_BOX_COORDINATES] = smoothed_corners
            ret.xyxy = xyxyxyxy_to_xyxy(smoothed_corners)
        else:
            ret.data.pop(ORIENTED_BOX_COORDINATES, None)

        return ret

    def get_smoothed_detections(self, track_ids: set[int] | None = None) -> Detections:
        """Return the smoothed detections for the requested active tracks.

        Args:
            track_ids: Optional set of track IDs to include in the output. When
                provided, tracks absent from the current frame are excluded from the
                emitted detections but their history stays cached.
        """
        tracked_detections = []
        for track_id in self.tracks:
            if track_ids is not None and track_id not in track_ids:
                continue
            track = self.get_track(track_id)
            if track is not None:
                tracked_detections.append(track)

        # Detections.merge requires all-or-none for optional fields.
        # When tracks disagree on confidence presence, drop it from all to
        # prevent ValueError inside Detections.merge (stack_or_none invariant).
        if tracked_detections and any(d.confidence is None for d in tracked_detections):
            for d in tracked_detections:
                d.confidence = None

        detections = Detections.merge(tracked_detections)
        if len(detections) == 0:
            detections.tracker_id = np.array([], dtype=int)

        return detections

Methods:

__init__(length: int = 5) -> None

Parameters:

Name	Type	Description	Default
length	int	The maximum number of frames to consider for smoothing detections. Each update counts as one frame, including frames without tracker IDs. Defaults to 5.	5

Source code in src/supervision/detection/tools/smoother.py

def __init__(self, length: int = 5) -> None:
    """
    Args:
        length: The maximum number of frames to consider for smoothing
            detections. Each update counts as one frame, including frames
            without tracker IDs. Defaults to 5.
    """
    self.tracks: defaultdict[int, deque[Detections | None]] = defaultdict(
        lambda: deque(maxlen=length)
    )

get_smoothed_detections(track_ids: set[int] | None = None) -> Detections

Return the smoothed detections for the requested active tracks.

Parameters:

Name	Type	Description	Default
track_ids	set[int] | None	Optional set of track IDs to include in the output. When provided, tracks absent from the current frame are excluded from the emitted detections but their history stays cached.	None

Source code in src/supervision/detection/tools/smoother.py

def get_smoothed_detections(self, track_ids: set[int] | None = None) -> Detections:
    """Return the smoothed detections for the requested active tracks.

    Args:
        track_ids: Optional set of track IDs to include in the output. When
            provided, tracks absent from the current frame are excluded from the
            emitted detections but their history stays cached.
    """
    tracked_detections = []
    for track_id in self.tracks:
        if track_ids is not None and track_id not in track_ids:
            continue
        track = self.get_track(track_id)
        if track is not None:
            tracked_detections.append(track)

    # Detections.merge requires all-or-none for optional fields.
    # When tracks disagree on confidence presence, drop it from all to
    # prevent ValueError inside Detections.merge (stack_or_none invariant).
    if tracked_detections and any(d.confidence is None for d in tracked_detections):
        for d in tracked_detections:
            d.confidence = None

    detections = Detections.merge(tracked_detections)
    if len(detections) == 0:
        detections.tracker_id = np.array([], dtype=int)

    return detections

get_track(track_id: int) -> Detections | None

Return the smoothed :class:~supervision.detection.core.Detections for a single track.

Averages xyxy over all valid (non-None) frames in the track window. confidence is averaged only over frames that carry it; frames with confidence=None are excluded. When every valid frame carries compatible oriented-box corners, those corners are aligned and averaged too, and xyxy is derived from their resulting envelope. Mixed or incompatible oriented-box metadata is dropped to keep the returned geometry consistent. Every other field, such as class_id, data and metadata, is taken from the track's most recent frame. Returns None when the track is unknown or its entire window is empty.

Parameters:

Name	Type	Description	Default
track_id	int	The tracker ID whose smoothed detection to retrieve.	required

Returns:

Name	Type	Description
Smoothed	Detections | None	class:~supervision.detection.core.Detections for the
	Detections | None	track, or None if the track is unknown or all frames in its window
	Detections | None	are empty.

Source code in src/supervision/detection/tools/smoother.py

def get_track(self, track_id: int) -> Detections | None:
    """Return the smoothed :class:`~supervision.detection.core.Detections` for a
    single track.

    Averages `xyxy` over all valid (non-`None`) frames in the track window.
    `confidence` is averaged only over frames that carry it; frames with
    `confidence=None` are excluded. When every valid frame carries compatible
    oriented-box corners, those corners are aligned and averaged too, and
    `xyxy` is derived from their resulting envelope. Mixed or incompatible
    oriented-box metadata is dropped to keep the returned geometry consistent.
    Every other field, such as `class_id`, `data` and `metadata`, is taken from
    the track's most recent frame.
    Returns `None` when the track is unknown or its entire window is empty.

    Args:
        track_id: The tracker ID whose smoothed detection to retrieve.

    Returns:
        Smoothed :class:`~supervision.detection.core.Detections` for the
        track, or `None` if the track is unknown or all frames in its window
        are empty.
    """
    track = self.tracks.get(track_id, None)
    if track is None:
        return None

    valid: list[Detections] = [d for d in track if d is not None]
    if len(valid) == 0:
        return None

    # Build on the newest frame: for a track active in the current frame that is
    # the current detection, so its class and data are up to date, and every
    # emitted track carries the same frame's metadata, which `Detections.merge`
    # requires to agree.
    ret = deepcopy(valid[-1])
    ret.xyxy = np.mean(np.stack([d.xyxy for d in valid], axis=0), axis=0)
    # Average confidence only over frames that carry it; frames with
    # confidence=None contribute nothing to the mean. Retain None when
    # no frame in the window carries confidence.
    confidences = [d.confidence for d in valid if d.confidence is not None]
    ret.confidence = np.mean(np.array(confidences), axis=0) if confidences else None

    corner_sets = [
        np.asarray(d.data[ORIENTED_BOX_COORDINATES])
        for d in valid
        if ORIENTED_BOX_COORDINATES in d.data
    ]
    if len(corner_sets) == len(valid) and len({c.shape for c in corner_sets}) == 1:
        reference = corner_sets[0]
        aligned = [
            _align_oriented_corners(corners, reference) for corners in corner_sets
        ]
        smoothed_corners = np.mean(np.stack(aligned, axis=0), axis=0)
        ret.data[ORIENTED_BOX_COORDINATES] = smoothed_corners
        ret.xyxy = xyxyxyxy_to_xyxy(smoothed_corners)
    else:
        ret.data.pop(ORIENTED_BOX_COORDINATES, None)

    return ret

reset() -> None

Clears the per-track detection history so the smoother can be reused across independent streams without carrying over frames from a previous stream. The configured window length is preserved.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> smoother = sv.DetectionsSmoother()
>>> detections = sv.Detections(
...     xyxy=np.array([[0, 0, 10, 10]]),
...     confidence=np.array([0.5]),
...     tracker_id=np.array([1])
... )
>>> _ = smoother.update_with_detections(detections)
>>> len(smoother.tracks)
1
>>> smoother.reset()
>>> len(smoother.tracks)
0

Source code in src/supervision/detection/tools/smoother.py

def reset(self) -> None:
    """Clears the per-track detection history so the smoother can be reused across
    independent streams without carrying over frames from a previous stream. The
    configured window `length` is preserved.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> smoother = sv.DetectionsSmoother()
        >>> detections = sv.Detections(
        ...     xyxy=np.array([[0, 0, 10, 10]]),
        ...     confidence=np.array([0.5]),
        ...     tracker_id=np.array([1])
        ... )
        >>> _ = smoother.update_with_detections(detections)
        >>> len(smoother.tracks)
        1
        >>> smoother.reset()
        >>> len(smoother.tracks)
        0

        ```
    """
    self.tracks.clear()

update_with_detections(detections: Detections) -> Detections

Updates the smoother with a new set of detections from a frame.

Every call counts as one frame toward the window length. Frames without tracker IDs, including empty ones, still age the cached history, but their detections are returned unchanged and each such frame emits a warning. The warning is raised before the history changes, so a warning turned into an error leaves the cached history intact.

Parameters:

Name	Type	Description	Default
detections	Detections	The detections to add to the smoother.	required

Returns:

Type	Description
Detections	Smoothed detections for the tracks present in this frame. When
Detections	detections has no tracker_id, the input object itself is returned.

Source code in src/supervision/detection/tools/smoother.py

def update_with_detections(self, detections: Detections) -> Detections:
    """Updates the smoother with a new set of detections from a frame.

    Every call counts as one frame toward the window `length`. Frames without
    tracker IDs, including empty ones, still age the cached history, but their
    detections are returned unchanged and each such frame emits a warning. The
    warning is raised before the history changes, so a warning turned into an
    error leaves the cached history intact.

    Args:
        detections: The detections to add to the smoother.

    Returns:
        Smoothed detections for the tracks present in this frame. When
        `detections` has no `tracker_id`, the input object itself is returned.
    """
    tracker_ids = detections.tracker_id
    if tracker_ids is None:
        warnings.warn(
            "Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
            "https://trackers.roboflow.com/latest/ for more "
            "information.",
            category=SupervisionWarnings,
        )
        self._age_tracks(active_ids=set())
        return detections

    for detection_idx in range(len(detections)):
        tracker_id = int(tracker_ids[detection_idx])
        self.tracks[tracker_id].append(detections.select(detection_idx))

    active_ids = set(tracker_ids.tolist())
    self._age_tracks(active_ids=active_ids)
    return self.get_smoothed_detections(track_ids=active_ids)

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