# Yolov5_DeepSort_Pytorch **Repository Path**: icehole/Yolov5_DeepSort_Pytorch ## Basic Information - **Project Name**: Yolov5_DeepSort_Pytorch - **Description**: No description available - **Primary Language**: Unknown - **License**: AGPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-11-12 - **Last Updated**: 2025-11-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # **BoxMOT**: Pluggable SOTA multi-object tracking modules for segmentation, object detection and pose estimation models
BoxMot demo
mikel-brostrom%2Fboxmot | Trendshift [![CI](https://github.com/mikel-brostrom/yolov8_tracking/actions/workflows/ci.yml/badge.svg)](https://github.com/mikel-brostrom/yolov8_tracking/actions/workflows/ci.yml) [![PyPI version](https://badge.fury.io/py/boxmot.svg)](https://badge.fury.io/py/boxmot) [![downloads](https://static.pepy.tech/badge/boxmot)](https://pepy.tech/project/boxmot) [![license](https://img.shields.io/badge/license-AGPL%203.0-blue)](https://github.com/mikel-brostrom/boxmot/blob/master/LICENSE) [![python-version](https://img.shields.io/pypi/pyversions/boxmot)](https://badge.fury.io/py/boxmot) [![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/18nIqkBr68TkK8dHdarxTco6svHUJGggY?usp=sharing) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8132989.svg)](https://doi.org/10.5281/zenodo.8132989) [![docker pulls](https://img.shields.io/docker/pulls/boxmot/boxmot?logo=docker)](https://hub.docker.com/r/boxmot/boxmot) [![discord](https://img.shields.io/discord/1377565354326495283?logo=discord&label=discord&labelColor=fff&color=5865f2)](https://discord.gg/tUmFEcYU4q) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/mikel-brostrom/boxmot)
## πŸš€ Key Features - **Pluggable Architecture** Easily swap in/out SOTA multi-object trackers. - **Universal Model Support** Integrate with any segmentation, object-detection and pose-estimation models that outputs bounding boxes - **Benchmark-Ready** Local evaluation pipelines for MOT17, MOT20, and DanceTrack ablation datasets with "official" ablation detectors - **Performance Modes** - **Motion-only**: for lightweight, CPU-efficient, high-FPS performance - **Motion + Appearance**: Combines motion cues with appearance embeddings ([CLIPReID](https://arxiv.org/pdf/2211.13977.pdf), [LightMBN](https://arxiv.org/pdf/2101.10774.pdf), [OSNet](https://arxiv.org/pdf/1905.00953.pdf)) to maximize identity consistency and accuracy at a higher computational cost - **Reusable Detections & Embeddings** Save once, run evaluations with no redundant preprocessing lightning fast. ## πŸ“Š Benchmark Results (MOT17 ablation split)
| Tracker | Status | HOTA↑ | MOTA↑ | IDF1↑ | FPS | | :-----: | :-----: | :---: | :---: | :---: | :---: | | [boosttrack](https://arxiv.org/abs/2408.13003) | βœ… | 69.253 | 75.914 | 83.206 | 25 | | [botsort](https://arxiv.org/abs/2206.14651) | βœ… | 68.885 | 78.222 | 81.344 | 46 | | [hybridsort](https://arxiv.org/abs/2308.00783) | βœ… | 68.216 | 76.382 | 81.164 | 25 | | [strongsort](https://arxiv.org/abs/2202.13514) | βœ… | 68.05 | 76.185 | 80.763 | 17 | | [deepocsort](https://arxiv.org/abs/2302.11813) | βœ… | 67.796 | 75.868 | 80.514 | 12 | | [bytetrack](https://arxiv.org/abs/2110.06864) | βœ… | 67.68 | 78.039 | 79.157 | 1265 | | [ocsort](https://arxiv.org/abs/2203.14360) | βœ… | 66.441 | 74.548 | 77.899 | 1483 | NOTES: Evaluation was conducted on the second half of the MOT17 training set, as the validation set is not publicly available and the ablation detector was trained on the first half. We employed [pre-generated detections and embeddings](https://github.com/mikel-brostrom/boxmot/releases/download/v11.0.9/runs2.zip). Each tracker was configured using the default parameters from their official repositories.
## πŸ”§ Installation Install the `boxmot` package, including all requirements, in a Python>=3.9 environment: ```bash pip install boxmot ``` If you want to contribute to this package check how to contribute [here](https://github.com/mikel-brostrom/boxmot/blob/master/CONTRIBUTING.md) ## πŸ’» CLI BoxMOT provides a unified CLI `boxmot` with the following subcommands: ```bash Usage: boxmot COMMAND [ARGS]... Commands: eval Evaluate tracking performance export Export ReID models generate Generate detections and embeddings track Run tracking only tune Tune models via evolutionary algorithms ``` ## 🐍 PYTHON Seamlessly integrate BoxMOT directly into your Python MOT applications with your custom model. ```python import cv2 import torch import numpy as np from pathlib import Path from boxmot import BoostTrack from torchvision.models.detection import ( fasterrcnn_resnet50_fpn_v2, FasterRCNN_ResNet50_FPN_V2_Weights as Weights ) # Set device device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Load detector with pretrained weights and preprocessing transforms weights = Weights.DEFAULT detector = fasterrcnn_resnet50_fpn_v2(weights=weights, box_score_thresh=0.5) detector.to(device).eval() transform = weights.transforms() # Initialize tracker tracker = BoostTrack(reid_weights=Path('osnet_x0_25_msmt17.pt'), device=device, half=False) # Start video capture cap = cv2.VideoCapture(0) with torch.inference_mode(): while True: success, frame = cap.read() if not success: break # Convert frame to RGB and prepare for detector rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) tensor = torch.from_numpy(rgb).permute(2, 0, 1).to(torch.uint8) input_tensor = transform(tensor).to(device) # Run detection output = detector([input_tensor])[0] scores = output['scores'].cpu().numpy() keep = scores >= 0.5 # Prepare detections for tracking boxes = output['boxes'][keep].cpu().numpy() labels = output['labels'][keep].cpu().numpy() filtered_scores = scores[keep] detections = np.concatenate([boxes, filtered_scores[:, None], labels[:, None]], axis=1) # Update tracker and draw results # INPUT: M X (x, y, x, y, conf, cls) # OUTPUT: M X (x, y, x, y, id, conf, cls, ind) res = tracker.update(detections, frame) tracker.plot_results(frame, show_trajectories=True) # Show output cv2.imshow('BoXMOT + Torchvision', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # Clean up cap.release() cv2.destroyAllWindows() ``` ## πŸ“ Code Examples & Tutorials
Tracking ```bash $ boxmot track --yolo-model rf-detr-base.pt # bboxes only boxmot track --yolo-model yolox_s.pt # bboxes only boxmot track --yolo-model yolo12n.pt # bboxes only boxmot track --yolo-model yolo11n.pt # bboxes only boxmot track --yolo-model yolov10n.pt # bboxes only boxmot track --yolo-model yolov9c.pt # bboxes only boxmot track --yolo-model yolov8n.pt # bboxes only yolov8n-seg.pt # bboxes + segmentation masks yolov8n-pose.pt # bboxes + pose estimation ```
Tracking methods ```bash $ boxmot track --tracking-method deepocsort strongsort ocsort bytetrack botsort boosttrack ```
Tracking sources Tracking can be run on most video formats ```bash $ boxmot track --source 0 # webcam img.jpg # image vid.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ```
Select ReID model Some tracking methods combine appearance description and motion in the process of tracking. For those which use appearance, you can choose a ReID model based on your needs from this [ReID model zoo](https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO). These model can be further optimized for you needs by the [reid_export.py](https://github.com/mikel-brostrom/boxmot/blob/master/boxmot/appearance/reid/export.py) script ```bash $ boxmot track --source 0 --reid-model lmbn_n_cuhk03_d.pt # lightweight osnet_x0_25_market1501.pt mobilenetv2_x1_4_msmt17.engine resnet50_msmt17.onnx osnet_x1_0_msmt17.pt clip_market1501.pt # heavy clip_vehicleid.pt ... ```
Filter tracked classes By default the tracker tracks all MS COCO classes. If you want to track a subset of the classes that you model predicts, add their corresponding index after the classes flag, ```bash boxmot track --source 0 --yolo-model yolov8s.pt --classes 16 17 # COCO yolov8 model. Track cats and dogs, only ``` [Here](https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/) is a list of all the possible objects that a Yolov8 model trained on MS COCO can detect. Notice that the indexing for the classes in this repo starts at zero
Evaluation Evaluate a combination of detector, tracking method and ReID model on standard MOT dataset or you custom one by ```bash # reproduce MOT17 README results $ boxmot eval --yolo-model yolox_x_MOT17_ablation.pt --reid-model lmbn_n_duke.pt --tracking-method boosttrack --source MOT17-ablation --verbose # MOT20 results $ boxmot eval --yolo-model yolox_x_MOT20_ablation.pt --reid-model lmbn_n_duke.pt --tracking-method boosttrack --source MOT20-ablation --verbose # Dancetrack results $ boxmot eval --yolo-model yolox_x_dancetrack_ablation.pt --reid-model lmbn_n_duke.pt --tracking-method boosttrack --source dancetrack-ablation --verbose # metrics on custom dataset $ boxmot eval --yolo-model yolov8n.pt --reid-model osnet_x0_25_msmt17.pt --tracking-method deepocsort --source ./assets/MOT17-mini/train --verbose ``` add `--gsi` to your command for postprocessing the MOT results by gaussian smoothed interpolation. Detections and embeddings are stored for the selected YOLO and ReID model respectively. They can then be loaded into any tracking algorithm. Avoiding the overhead of repeatedly generating this data.
Evolution We use a fast and elitist multiobjective genetic algorithm for tracker hyperparameter tuning. By default the objectives are: HOTA, MOTA, IDF1. Run it by ```bash # saves dets and embs under ./runs/dets_n_embs separately for each selected yolo and reid model $ boxmot generate --source ./assets/MOT17-mini/train --yolo-model yolov8n.pt yolov8s.pt --reid-model weights/osnet_x0_25_msmt17.pt # evolve parameters for specified tracking method using the selected detections and embeddings generated in the previous step $ boxmot tune --yolo-model yolov8n.pt --reid-model osnet_x0_25_msmt17.pt --n-trials 9 --tracking-method botsort --source ./assets/MOT17-mini/train ``` The set of hyperparameters leading to the best HOTA result are written to the tracker's config file.
Export We support ReID model export to ONNX, OpenVINO, TorchScript and TensorRT ```bash # export to ONNX $ boxmot export --weights weights/osnet_x0_25_msmt17.pt --include onnx --device cpu # export to OpenVINO $ boxmot export --weights weights/osnet_x0_25_msmt17.pt --include openvino --device cpu # export to TensorRT with dynamic input $ boxmot export --weights weights/osnet_x0_25_msmt17.pt --include engine --device 0 --dynamic ```
| Example Description | Notebook | |---------------------|----------| | Torchvision bounding box tracking with BoxMOT | [![Notebook](https://img.shields.io/badge/Notebook-torchvision_det_boxmot.ipynb-blue)](examples/det/torchvision_boxmot.ipynb) | | Torchvision pose tracking with BoxMOT | [![Notebook](https://img.shields.io/badge/Notebook-torchvision_pose_boxmot.ipynb-blue)](examples/pose/torchvision_boxmot.ipynb) | | Torchvision segmentation tracking with BoxMOT | [![Notebook](https://img.shields.io/badge/Notebook-torchvision_seg_boxmot.ipynb-blue)](examples/seg/torchvision_boxmot.ipynb) |
## Contributors ## Contact For BoxMOT bugs and feature requests please visit [GitHub Issues](https://github.com/mikel-brostrom/boxmot/issues). For business inquiries or professional support requests please send an email to: box-mot@outlook.com