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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

  • 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 ultralytics import YOLO

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 = YOLO("<MODEL_PATH>")
tracker = sv.ByteTrack(frame_rate=video_info.fps)
smoother = sv.DetectionsSmoother()

box_annotator = sv.BoxAnnotator()

with sv.VideoSink("<TARGET_FILE_PATH>", video_info=video_info) as sink:
    for frame in frame_generator:
        result = model(frame)[0]
        detections = sv.Detections.from_ultralytics(result)
        detections = tracker.update_with_detections(detections)
        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 
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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

        - `DetectionsSmoother` requires the `tracker_id` for each detection. Refer to
          [Roboflow Trackers](/latest/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:
        ```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 ultralytics import YOLO

        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 = YOLO("<MODEL_PATH>")
        tracker = sv.ByteTrack(frame_rate=video_info.fps)
        smoother = sv.DetectionsSmoother()

        box_annotator = sv.BoxAnnotator()

        with sv.VideoSink("<TARGET_FILE_PATH>", video_info=video_info) as sink:
            for frame in frame_generator:
                result = model(frame)[0]
                detections = sv.Detections.from_ultralytics(result)
                detections = tracker.update_with_detections(detections)
                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. Defaults to 5.
        """
        self.tracks: defaultdict[int, deque[Detections | None]] = defaultdict(
            lambda: deque(maxlen=length)
        )

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

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

        if detections.tracker_id is None:
            warnings.warn(
                "Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
                "https://supervision.roboflow.com/latest/trackers for more "
                "information.",
                category=SupervisionWarnings,
            )
            return detections

        for detection_idx in range(len(detections)):
            tracker_id_value = detections.tracker_id[detection_idx]
            tracker_id = int(tracker_id_value)

            self.tracks[tracker_id].append(cast(Detections, detections[detection_idx]))

        for track_id in self.tracks.keys():
            if track_id not in detections.tracker_id:
                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]

        return self.get_smoothed_detections()

    def get_track(self, track_id: int) -> Detections | None:
        """Return the smoothed `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. 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 `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

        ret = deepcopy(valid[0])
        ret.xyxy = np.mean([d.xyxy for d in valid], 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

        return ret

    def get_smoothed_detections(self) -> Detections:
        tracked_detections = []
        for track_id in self.tracks:
            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

Functions

__init__(length: int = 5) -> None

Parameters:

Name Type Description Default
length
int

The maximum number of frames to consider for smoothing detections. Defaults to 5.

 5
Source code in src/supervision/detection/tools/smoother.py 
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def __init__(self, length: int = 5) -> None:
    """
    Args:
        length: The maximum number of frames to consider for smoothing
            detections. Defaults to 5.
    """
    self.tracks: defaultdict[int, deque[Detections | None]] = defaultdict(
        lambda: deque(maxlen=length)
    )

get_track(track_id: int) -> Detections | None

Return the smoothed 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. 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:

Type Description
Detections | None

Smoothed Detections for the track, or None if the track is
Detections | None

unknown or all frames in its window are empty.
Source code in src/supervision/detection/tools/smoother.py 
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def get_track(self, track_id: int) -> Detections | None:
    """Return the smoothed `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. 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 `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

    ret = deepcopy(valid[0])
    ret.xyxy = np.mean([d.xyxy for d in valid], 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

    return ret

update_with_detections(detections: Detections) -> Detections

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

Parameters:

Name Type Description Default
detections
Detections

The detections to add to the smoother.

 required
Source code in src/supervision/detection/tools/smoother.py 
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def update_with_detections(self, detections: Detections) -> Detections:
    """
    Updates the smoother with a new set of detections from a frame.

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

    if detections.tracker_id is None:
        warnings.warn(
            "Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
            "https://supervision.roboflow.com/latest/trackers for more "
            "information.",
            category=SupervisionWarnings,
        )
        return detections

    for detection_idx in range(len(detections)):
        tracker_id_value = detections.tracker_id[detection_idx]
        tracker_id = int(tracker_id_value)

        self.tracks[tracker_id].append(cast(Detections, detections[detection_idx]))

    for track_id in self.tracks.keys():
        if track_id not in detections.tracker_id:
            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]

    return self.get_smoothed_detections()

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