index.md

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Plug-and-play multi-object tracking for any detection model. Clean, modular implementations of SORT, ByteTrack, and OC-SORT under the Apache 2.0 license.

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Install

Get started by installing the package.

pip install trackers

For more options, see the install guide.

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Track from CLI

Point at a video, webcam, RTSP stream, or image directory. Get tracked output.

trackers track \
    --source video.mp4 \
    --output output.mp4 \
    --model rfdetr-medium \
    --tracker bytetrack \
    --show-labels \
    --show-trajectories

For all CLI options, see the tracking guide.

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Track from Python

Plug trackers into your existing detection pipeline. Works with any detector.

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

model = get_model(model_id="rfdetr-medium")
tracker = ByteTrackTracker()

cap = cv2.VideoCapture("video.mp4")
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

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

For more examples, see the tracking guide.

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Evaluate

Benchmark your tracker against ground truth with standard MOT metrics.

trackers eval \
    --gt-dir ./data/mot17/val \
    --tracker-dir results \
    --metrics CLEAR HOTA Identity \
    --columns MOTA HOTA IDF1

Sequence                        MOTA    HOTA    IDF1
----------------------------------------------------
MOT17-02-FRCNN                30.192  35.475  38.515
MOT17-04-FRCNN                48.912  55.096  61.854
MOT17-05-FRCNN                52.755  45.515  55.705
MOT17-09-FRCNN                51.441  50.108  57.038
MOT17-10-FRCNN                51.832  49.648  55.797
MOT17-11-FRCNN                55.501  49.401  55.061
MOT17-13-FRCNN                60.488  58.651  69.884
----------------------------------------------------
COMBINED                      47.406  50.355  56.600

For the full evaluation workflow, see the evaluation guide.

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Algorithms

Clean, modular implementations of leading trackers. All HOTA scores use default parameters.

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

For detailed benchmarks and tuned configurations, see the tracker comparison.

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Download Datasets

Pull benchmark datasets for evaluation with a single command.

trackers download mot17 \
    --split val \
    --asset annotations,detections

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

For more download options, see the download guide.

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Try It

Try trackers in your browser with our Hugging Face Playground.

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Tutorials

• How to Track Objects with SORT

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  End-to-end example showing how to run RF-DETR detection with the SORT tracker.

  :simple-googlecolab: Run Google Colab

• How to Track Objects with ByteTrack

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  End-to-end example showing how to run RF-DETR detection with the ByteTrack tracker.

  :simple-googlecolab: Run Google Colab

• How to Track Objects with OC-SORT

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  End-to-end example showing how to run RF-DETR detection with the OC-SORT tracker.

  :simple-googlecolab: Run Google Colab