boxmat_lite
a dirty change of boxmat, minimal for bytetrack
Science Score: 67.0%
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Repository
a dirty change of boxmat, minimal for bytetrack
Basic Info
- Host: GitHub
- Owner: yyhtbs-yye
- License: agpl-3.0
- Language: Python
- Default Branch: main
- Size: 233 KB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
[](https://discord.gg/m4szuBcT)
Introduction
This repository addresses the fragmented nature of the multi-object tracking (MOT) field by providing a standardized collection of pluggable, state-of-the-art trackers. Designed to seamlessly integrate with segmentation, object detection, and pose estimation models, the repository streamlines the adoption and comparison of MOT methods. For trackers employing appearance-based techniques, we offer a range of automatically downloadable state-of-the-art re-identification (ReID) models, from heavyweight (CLIPReID) to lightweight options (LightMBN, OSNet). Additionally, clear and practical examples demonstrate how to effectively integrate these trackers with various popular models, enabling versatility across diverse vision tasks.
Why BOXMOT?
Multi-object tracking solutions today depend heavily on the computational capabilities of the underlying hardware. BoxMOT addresses this by offering a wide array of tracking methods tailored to accommodate diverse hardware constraints, ranging from CPU-only setups to high-end GPUs. Furthermore, we provide scripts designed for rapid experimentation, enabling users to save detections and embeddings once and subsequently reuse them with any tracking algorithm. This approach eliminates redundant computations, significantly speeding up the evaluation and comparison of multiple trackers.
Installation
Install the boxmot package, including all requirements, in a Python>=3.9 environment:
bash
pip install boxmot
BoxMOT provides a unified CLI boxmot with the following subcommands:
```bash Usage: boxmot COMMAND [ARGS]...
Commands: track Run tracking only generate-dets-embs Generate detections and embeddings generate-mot-results Generate MOT evaluation results based on pregenerated detecions and embeddings eval Evaluate tracking performance using the official trackeval repository tune Tune tracker hyperparameters based on selected detections and embeddings ```
YOLOv12 | YOLOv11 | YOLOv10 | YOLOv9 | YOLOv8 | RFDETR | YOLOX examples
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/yolo_tracking/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 zeroEvaluation
Evaluate a combination of detector, tracking method and ReID model on standard MOT dataset or you custom one by ```bash $ boxmot eval --yolo-model yolov8n.pt --reid-model osnet_x0_25_msmt17.pt --tracking-method deepocsort --verbose --source ./assets/MOT17-mini/train $ boxmot eval --yolo-model yolov8n.pt --reid-model osnet_x0_25_msmt17.pt --tracking-method ocsort --verbose --source ./tracking/val_utils/MOT17/train ``` 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-dets-embs --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 --dets yolov8n --embs osnet_x0_25_msmt17 --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 $ python3 boxmot/appearance/reid_export.py --include onnx --device cpu # export to OpenVINO $ python3 boxmot/appearance/reid_export.py --include openvino --device cpu # export to TensorRT with dynamic input $ python3 boxmot/appearance/reid_export.py --include engine --device 0 --dynamic ```Custom tracking examples
Contributors
Contact
For BoxMOT bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please send an email to: box-mot@outlook.com
Owner
- Name: Yuhang Ye
- Login: yyhtbs-yye
- Kind: user
- Repositories: 8
- Profile: https://github.com/yyhtbs-yye
Citation (CITATION.cff)
cff-version: 13.0.0
preferred-citation:
type: software
message: "If you use Yolo Tracking, please cite it as below."
authors:
- family-names: Broström
given-names: Mikel
title: "BoxMOT: pluggable SOTA tracking modules for object detection, segmentation and pose estimation models"
version: 13.0.0
doi: https://zenodo.org/record/7629840
date-released: 2024-6
license: AGPL-3.0
url: "https://github.com/mikel-brostrom/boxmot"
GitHub Events
Total
- Push event: 6
- Pull request event: 3
- Create event: 5
Last Year
- Push event: 6
- Pull request event: 3
- Create event: 5
Dependencies
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- python 3.9-slim build
- PyYAML ==6.0.2
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- lap ==0.5.12
- loguru ==0.7.3
- numpy ==1.24.4
- opencv-python ==4.11.0.86
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- scipy ==1.13.1
- threadpoolctl ==3.6.0
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