trackers

Trackers gives you clean, modular re-implementations of leading multi-object tracking algorithms released under the permissive Apache 2.0 license. You combine them with any detection model you already use.

trackers-2.0.0-promo.mp4

Install

You can install and use trackers in a Python>=3.10 environment. For detailed installation instructions, including installing from source and setting up a local development environment, check out our install page.

pip install trackers

install from source

By installing trackers from source, you can explore the most recent features and enhancements that have not yet been officially released. Please note that these updates are still in development and may not be as stable as the latest published release.

pip install https://github.com/roboflow/trackers/archive/refs/heads/develop.zip

Tracking Algorithms

Trackers gives you clean, modular re-implementations of leading multi-object tracking algorithms. The package currently supports SORT and ByteTrack. OC-SORT support is coming soon. For full results, see the benchmarks page.

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
OC-SORT	OCSORTTracker	—	—	—	—	—

Quickstart

With a modular design, Trackers lets you combine object detectors from different libraries with the tracker of your choice. Here's how you can use ByteTrack with various detectors. These examples use OpenCV for decoding and display. Replace <SOURCE_VIDEO_PATH> with your input.

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()

run with Inference

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

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

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)
    result = model.infer(frame_rgb)[0]
    detections = sv.Detections.from_inference(result)
    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("Inference + ByteTrack", annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

run with Ultralytics

import cv2
import supervision as sv
from ultralytics import YOLO
from trackers import ByteTrack

tracker = ByteTrack()
model = YOLO("yolo26m.pt")

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)
    result = model(frame_rgb)[0]
    detections = sv.Detections.from_ultralytics(result)
    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("Ultralytics + ByteTrack", annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

run with Transformers

import torch
import cv2
import supervision as sv
from trackers import ByteTrack
from transformers import RTDetrImageProcessor, RTDetrV2ForObjectDetection

tracker = ByteTrack()
processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd")

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)
    inputs = processor(images=frame_rgb, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)

    h, w = frame_bgr.shape[:2]
    results = processor.post_process_object_detection(
        outputs,
        target_sizes=torch.tensor([[h, w]]),
        threshold=0.5
    )[0]

    detections = sv.Detections.from_transformers(
        transformers_results=results,
        id2label=model.config.id2label
    )
    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("Transformers + ByteTrack", annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

License

The code is released under the Apache 2.0 license.

Contribution

We welcome all contributions—whether it’s reporting issues, suggesting features, or submitting pull requests. Please read our contributor guidelines to learn about our processes and best practices.