trackers 2.4.0rc0

A unified library for object tracking featuring clean room re-implementations of leading multi-object tracking algorithms

trackers

Plug-and-play multi-object tracking for any detection model.

https://github.com/user-attachments/assets/eef9b00a-cfe4-40f7-a495-954550e3ef1f

Install

pip install trackers

Install from source

pip install git+https://github.com/roboflow/trackers.git

For more options, see the install guide.

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.

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.

https://github.com/user-attachments/assets/d2347a25-469d-44cd-8049-d15274bd91ae

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.

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.

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.

Try It

Try trackers in your browser with our Hugging Face Playground.

Documentation

Full guides, API reference, and tutorials: trackers.roboflow.com

Contributing

We welcome contributions. Read our contributor guidelines to get started.

License

The code is released under the Apache 2.0 license.