Trackers 2.3.0

Changelog

🚀 Added

• Added OCSORTTracker, a clean re-implementation of OC-SORT. OC-SORT shifts to an observation-centric paradigm, using real detections to correct Kalman filter errors accumulated during occlusions. It introduces Observation-Centric Re-Update (ORU) for state recovery, Observation-Centric Momentum (OCM) for direction-consistency-weighted association, and Observation-Centric Recovery (OCR) for second-stage heuristic matching. OC-SORT achieves the highest HOTA on MOT17 and DanceTrack with default parameters. (#207)

Algorithm	Description	MOT17 HOTA	SportsMOT HOTA	SoccerNet HOTA	DanceTrack HOTA
SORT	Kalman filter + Hungarian matching baseline.	58.4	70.9	81.6	45.0
ByteTrack	Two-stage association using high and low confidence detections.	60.1	73.0	84.0	50.2
OC-SORT	Observation-centric recovery for lost tracks.	61.9	71.7	78.4	51.8

import cv2
import supervision as sv
from inference import get_model
from trackers import OCSORTTracker

model = get_model("rfdetr-medium")
tracker = OCSORTTracker()

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

cap = cv2.VideoCapture("<SOURCE_VIDEO_PATH>")
if not cap.isOpened():
    raise RuntimeError("Failed to open video source")

while True:
    ret, frame = cap.read()
    if not ret:
        break

    result = model.infer(frame)[0]
    detections = sv.Detections.from_inference(result)
    detections = tracker.update(detections)

    frame = box_annotator.annotate(frame, detections)
    frame = label_annotator.annotate(frame, detections, labels=detections.tracker_id)

    cv2.imshow("RF-DETR + OC-SORT", frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

trackers-2.3.0-promo.mp4

• Added trackers download CLI command and download_dataset Python API. Download benchmark datasets directly from the command line or from code. Supports MOT17 and SportsMOT with split and asset filtering. (#262)

# List available datasets
trackers download --list

# Download full dataset
trackers download mot17

# Download specific split and asset type
trackers download mot17 --split train --annotations-only

# Custom output directory
trackers download sportsmot --split val -o ./datasets

from trackers import download_dataset, Dataset, DatasetSplit, DatasetAsset

download_dataset(
    dataset=Dataset.MOT17,
    split=[DatasetSplit.VAL],
    asset=[DatasetAsset.ANNOTATIONS, DatasetAsset.DETECTIONS],
    output_dir="./data",
)

Dataset	Description	Splits	Assets	License
mot17	Pedestrian tracking with crowded scenes and frequent occlusions.	train, val, test	frames, annotations, detections	CC BY-NC-SA 3.0
sportsmot	Sports broadcast tracking with fast motion and similar-looking targets.	train, val, test	frames, annotations	CC BY 4.0

MOT17_MOT17-02-DPM.mp4

SportsMOT_v_-6Os86HzwCs_c001.mp4

• Added --track-ids flag to trackers track CLI command. Filter displayed tracks by track ID to focus on specific objects in a scene. (#280)

trackers track --source video.mp4 --output output.mp4 \
    --model rfdetr-medium \
    --tracker bytetrack \
    --track-ids 1,2

🌱 Changed

• Made --source optional in trackers track when --detections is provided and no visual output is requested, enabling frameless tracking for evaluation workflows. (#322)

• Optimized xcycsr_to_xyxy and xyxy_to_xcycsr bounding box converters for the single-box hot path, reducing per-call overhead in inner tracking loops. (#296)

🛠️ Fix

• Fixed a bug in MOT evaluation where ground-truth entries with conf=0 (distractors) were not filtered, causing artificially low scores on MOT17. Tracker entries with id < 0 are now also excluded. Results now match TrackEval exactly. (#322)

🏆 Contributors

@JVSCHANDRADITHYA (Chandradithya Janaswami), @salmanmkc (Salman Chishti), @AlexBodner (Alexander Bodner), @Borda (Jirka Borovec), @SkalskiP (Piotr Skalski)

Trackers 2.2.0

Changelog

🚀 Added

• Added camera motion compensation for stable trajectory visualization. (#263)

trackers-2.2.0-promo.mp4

• Added trackers track CLI command. Full tracking pipeline from the command line. Point it at a video, webcam, RTSP stream, or image directory. (#242, #230, #252, #243)

  trackers track --source video.mp4 --output output.mp4 \
      --model rfdetr-medium \
      --model.confidence 0.3 \
      --classes person \
      --show-labels --show-trajectories

• Added trackers eval CLI command. Evaluate tracker predictions against ground truth using standard MOT metrics. (#210, #211, #212, #214, #215, #223, #224, #226, #250)

  trackers eval \
      --gt-dir data/gt \
      --tracker-dir data/trackers \
      --metrics CLEAR HOTA Identity \
      --columns MOTA HOTA IDF1 IDSW

  Sequence                        MOTA    HOTA    IDF1  IDSW
  ----------------------------------------------------------
  MOT17-02-FRCNN                75.600  62.300  72.100    42
  MOT17-04-FRCNN                78.200  65.100  74.800    31
  ----------------------------------------------------------
  COMBINED                      75.033  62.400  72.033    73

• Added Trackers Playground on Hugging Face Spaces. Interactive Gradio demo with model and tracker selection, COCO class filtering, visualization flags, and cached examples. (#249)

• Added interactive CLI command builder to the docs. Generate trackers track commands with interactive controls. (#242)

🏆 Contributors

@omkar-334 (Omkar Kabde), @Aaryan2304 (Aaryan Kurade), @juan-cobos (Juan Cobos Álvarez), @Borda (Jirka Borovec), @SkalskiP (Piotr Skalski)

Trackers 2.1.0.

Changelog

Warning

Starting with version 2.1.0, Trackers package drops support for Python 3.9. If your environment still relies on Python 3.9, stay on Trackers 2.0.x or upgrade your Python runtime to 3.10 or newer.

Warning

Starting with version 2.1.0, the Trackers package drops support for DeepSORTTracker and ReIDModel. We plan to bring back improved ReID support in future releases.

🚀 Added

Added support for ByteTrack, a fast tracking by detection algorithm focused on stable identities under occlusion. We evaluated both SORT and ByteTrack implementations on three standard multiple object tracking benchmarks, MOT17, SportsMOT, and SoccerNet Tracking.

Algorithm	Trackers API	MOT17 HOTA	MOT17 IDF1	MOT17 MOTA	SportsMOT HOTA	SoccerNet HOTA
SORT	SORTTracker	58.4	69.9	67.2	70.9	81.6
ByteTrack	ByteTrackTracker	60.1	73.2	74.1	73.0	84.0

import cv2
import supervision as sv
from rfdetr import RFDETRMedium
from trackers import ByteTrack

tracker = ByteTrack()
model = RFDETRMedium()

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

video_capture = cv2.VideoCapture("<SOURCE_VIDEO_PATH>")
if not video_capture.isOpened():
    raise RuntimeError("Failed to open video source")

while True:
    success, frame_bgr = video_capture.read()
    if not success:
        break

    frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
    detections = model.predict(frame_rgb)
    detections = tracker.update(detections)

    annotated_frame = box_annotator.annotate(frame_bgr, detections)
    annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id)

    cv2.imshow("RF-DETR + ByteTrack", annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

rf-detr-1.4.0-and-trackers-2.1.0-promo.mp4

🏆 Contributors

@tstanczyk95 (Tomasz Stańczyk), @AlexBodner (Alexander Bodner), @Borda (Jirka Borovec), @SkalskiP (Piotr Skalski)