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 | 🚧 | 🚧 |
trackers-2.0.0-promo.mp4
Pip install the trackers package in a Python>=3.9 environment.
pip install trackersinstall 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 git+https://github.com/roboflow/trackers.gitWith 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:
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_VIDEO_PATH>", target_path="<OUTPUT_VIDEO_PATH>", callback=callback, )run with ultralytics
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_VIDEO_PATH>", target_path="<OUTPUT_VIDEO_PATH>", callback=callback, )run with transformers
import torch import supervision as sv from trackers import SORTTracker from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor tracker = SORTTracker() image_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 = image_processor(images=frame, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) h, w, _ = frame.shape results = image_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_VIDEO_PATH>", target_path="<OUTPUT_VIDEO_PATH>", callback=callback, )The code is released under the Apache 2.0 license.
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