trackers is a unified library offering clean room re-implementations of leading multi-object tracking algorithms. Its modular design allows you to easily swap trackers and integrate them with object detectors from various libraries like inference, ultralytics, or transformers.

Tracker	Paper	MOTA	Year	Status	Colab
SORT		74.6	2016	✅
DeepSORT		75.4	2017	✅
ByteTrack		77.8	2021	🚧	🚧
OC-SORT		75.9	2022	🚧	🚧
BoT-SORT		77.8	2022	🚧	🚧

Installation

You can install trackers in a Python>=3.9 environment.

Basic Installation

pippoetryuv

pip install trackers

poetry add trackers

uv pip install trackers

Hardware Acceleration

CPUCUDA 11.8CUDA 12.4CUDA 12.6ROCm 6.1ROCm 6.2.4

pip install "trackers[cpu]"

pip install "trackers[cu118]"

pip install "trackers[cu124]"

pip install "trackers[cu126]"

pip install "trackers[rocm61]"

pip install "trackers[rocm624]"

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 SORTTracker with various detectors:

inferencerf-detrultralyticstransformers

import supervision as sv
from trackers import SORTTracker
from inference import get_model

tracker = SORTTracker()
model = get_model(model_id="yolov11m-640")
annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)

def callback(frame, _):
    result = model.infer(frame)[0]
    detections = sv.Detections.from_inference(result)
    detections = tracker.update(detections)
    return annotator.annotate(frame, detections, labels=detections.tracker_id)

sv.process_video(
    source_path="input.mp4",
    target_path="output.mp4",
    callback=callback,
)

import supervision as sv
from trackers import SORTTracker
from rfdetr import RFDETRBase

tracker = SORTTracker()
model = RFDETRBase()
annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)

def callback(frame, _):
    detections = model.predict(frame)
    detections = tracker.update(detections)
    return annotator.annotate(frame, detections, labels=detections.tracker_id)

sv.process_video(
    source_path="input.mp4",
    target_path="output.mp4",
    callback=callback,
)

import supervision as sv
from trackers import SORTTracker
from ultralytics import YOLO

tracker = SORTTracker()
model = YOLO("yolo11m.pt")
annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)

def callback(frame, _):
    result = model(frame)[0]
    detections = sv.Detections.from_ultralytics(result)
    detections = tracker.update(detections)
    return annotator.annotate(frame, detections, labels=detections.tracker_id)

sv.process_video(
    source_path="input.mp4",
    target_path="output.mp4",
    callback=callback,
)

import torch
import supervision as sv
from trackers import SORTTracker
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

tracker = SORTTracker()
processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd")
annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)

def callback(frame, _):
    inputs = processor(images=frame, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)

    h, w, _ = frame.shape
    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)
    return annotator.annotate(frame, detections, labels=detections.tracker_id)

sv.process_video(
    source_path="input.mp4",
    target_path="output.mp4",
    callback=callback,
)

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