Usage for USVTrack

Installation

git clone https://github.com/yaoshanliang/yolo_tracking.git
cd yolo_tracking
conda create -n yolo_tracking python=3.8
conda activate yolo_tracking
pip install -v -e .
pip install boxmot

Trackers:

• bytetrack
• botsort
• strongsort
• ocsort
• deepocsort
• hybridsort

reid-model:

• osnet_x0_25_market1501.pt
• mobilenetv2_x1_4_msmt17.engine
• resnet50_msmt17.onnx
• osnet_x1_0_msmt17.pt
• clip_market1501.pt
• clip_vehicleid.pt

Evaluation

CUDA_VISIBLE_DEVICES=0 python examples/val.py --yolo-model /home/shanliang/workspace/code/yolov8/runs/USVTrack/YOLOv8n46/weights/best.pt --benchmark /home/shanliang/workspace/dataset/USVTrack/Tracking/ --split test --tracking-method hybridsort


# saves dets and embs under ./runs/dets_n_embs separately for each selected yolo and reid model
<!-- python tracking/generate_dets_n_embs.py --source ./assets/MOT17-mini/train --yolo-model yolov8n.pt yolov8s.pt --reid-model weights/osnet_x0_25_msmt17.pt -->
python tracking/generate_dets_n_embs.py --yolo-model /home/shanliang/workspace/code/yolov8/runs/USVTrack/YOLOv8n46/weights/best.pt --source /home/shanliang/workspace/dataset/USVTrack/Tracking/test --reid-model tracking/weights/osnet_x0_25_msmt17.pt

# generate MOT challenge format results based on pregenerated detections and embeddings for a specific trackign method
python tracking/generate_mot_results.py --dets yolov8n --embs osnet_x0_25_msmt17 --tracking-method botsort

# uses TrackEval to generate MOT metrics for the tracking results under ./runs/mot/<dets+embs+tracking-method>
$ python tracking/val.py --benchmark MOT17-mini --dets yolov8n --embs osnet_x0_25_msmt17 --tracking-method botsort

• Evaluate existing

CUDA_VISIBLE_DEVICES=0 python examples/val.py --yolo-model /home/shanliang/workspace/code/yolov8/runs/USVTrack/YOLOv8n46/weights/best.pt --benchmark /home/shanliang/workspace/dataset/USVTrack/Tracking/ --split test --tracking-method hybridsort --eval-existing --project runs/val --name USVTrack4

Visualization

CUDA_VISIBLE_DEVICES=0 python examples/val.py --yolo-model /home/shanliang/workspace/code/yolov8/runs/USVTrack/YOLOv8n46/weights/best.pt --benchmark /home/shanliang/workspace/dataset/USVTrack/Tracking/ --split demo --tracking-method hybridsort --save

Evolution

CUDA_VISIBLE_DEVICES=0 python examples/evolve.py --yolo-model /home/shanliang/workspace/code/yolov8/runs/USVTrack/YOLOv8n46/weights/best.pt  --benchmark /home/shanliang/workspace/dataset/USVTrack/Tracking/ --split test --tracking-method hybridsort --n-trials 100  # tune strongsort for USVTrack
                            --tracking-method ocsort     --benchmark <your-custom-dataset> --objective HOTA # tune ocsort for maximizing HOTA on your custom tracking dataset

---

BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

Introduction

This repo contains a collections of pluggable state-of-the-art multi-object trackers for segmentation, object detection and pose estimation models. For the methods using appearance description, both heavy (CLIPReID) and lightweight state-of-the-art ReID models (LightMBN, OSNet and more) are available for automatic download. We provide examples on how to use this package together with popular object detection models such as: Yolov8, Yolo-NAS and YOLOX.

Tracker	HOTA↑	MOTA↑	IDF1↑
BoTSORT	77.8	78.9	88.9
DeepOCSORT	77.4	78.4	89.0
OCSORT	77.4	78.4	89.0
HybridSORT	77.3	77.9	88.8
ByteTrack	75.6	74.6	86.0
StrongSORT

_{NOTES: performed on the 10 first frames of each MOT17 sequence. The detector used is ByteTrack's YoloXm, trained on: CrowdHuman, MOT17, Cityperson and ETHZ. Each tracker is configured with its original parameters found in their respective official repository.}

Tutorials

• Yolov8 training (link to external repository) 

• Deep appearance descriptor training (link to external repository) 

• ReID model export to ONNX, OpenVINO, TensorRT and TorchScript 

• Evaluation on custom tracking dataset 

• ReID inference acceleration with Nebullvm 

Experiments

In inverse chronological order:

• Evaluation of the params evolved for first half of MOT17 on the complete MOT17

• Segmentation model vs object detetion model on MOT metrics

• Effect of masking objects before feature extraction

• conf-thres vs HOTA, MOTA and IDF1

• Effect of KF updates ahead for tracks with no associations on MOT17

• Effect of full images vs 1280 input to StrongSORT on MOT17

• Effect of different OSNet architectures on MOT16

• Yolov5 StrongSORT vs BoTSORT vs OCSORT

  • Yolov5 BoTSORT branch: https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/tree/botsort

• Yolov5 StrongSORT OSNet vs other trackers MOT17 

• StrongSORT MOT16 ablation study 

• Yolov5 StrongSORT OSNet vs other trackers MOT16 (deprecated) 

News

• Enabled tracking per class for all trackers besides StrongSORT by --per-class (March 2024)
• Enabled trajectory plotting for all trackers besides StrongSORT by --show-trajectories (March 2024)
• All trackers inherit from BaseTracker (Mars 2024)
• Switched from setuptools to poetry for unified: dependency resolution, packaging and publishing management (March 2024)
• ~x3 pipeline speedup by: using pregenerated detections + embeddings and jobs parallelization (March 2024)
• Ultra fast exerimentation enabled by allowing local detections and embeddings saving. This data can then be loaded into any tracking algorithm, avoiding the overhead of repeatedly generating it (February 2024)
• Centroid-based cost function added to OCSORT and DeepOCSORT (suitable for: small and/or high speed objects and low FPS videos) (January 2024)
• Custom Ultralytics package updated from 8.0.124 to 8.0.224 (December 2023)
• HybridSORT available (August 2023)
• SOTA CLIP-ReID people and vehicle models available (August 2023)

Why BOXMOT?

Today's multi-object tracking options are heavily dependant on the computation capabilities of the underlaying hardware. BoxMOT provides a great variety of tracking methods that meet different hardware limitations, all the way from CPU only to larger GPUs. Morover, we provide scripts for ultra fast experimentation by saving detections and embeddings, which then be loaded into any tracking algorithm. Avoiding the overhead of repeatedly generating this data.

Installation

Start with Python>=3.8 environment.

If you want to run the YOLOv8, YOLO-NAS or YOLOX examples:

git clone https://github.com/mikel-brostrom/yolo_tracking.git
cd yolo_tracking
pip install poetry
poetry install --with yolo  # installed boxmot + yolo dependencies
poetry shell  # activates the newly created environment with the installed dependencies

but if you only want to import the tracking modules you can simply:

pip install boxmot

YOLOv8 | YOLO-NAS | YOLOX examples

Tracking

Yolo models

$ python tracking/track.py --yolo-model yolov8n       # bboxes only
  python tracking/track.py --yolo-model yolo_nas_s    # bboxes only
  python tracking/track.py --yolo-model yolox_n       # bboxes only
                                        yolov8n-seg   # bboxes + segmentation masks
                                        yolov8n-pose  # bboxes + pose estimation

Tracking methods

$ python tracking/track.py --tracking-method deepocsort
                                             strongsort
                                             ocsort
                                             bytetrack
                                             botsort

Tracking sources

Tracking can be run on most video formats

$ python tracking/track.py --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. These model can be further optimized for you needs by the reid_export.py script

$ python tracking/track.py --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,

python tracking/track.py --source 0 --yolo-model yolov8s.pt --classes 16 17  # COCO yolov8 model. Track cats and dogs, only

Here 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

# saves dets and embs under ./runs/dets_n_embs separately for each selected yolo and reid model
$ python tracking/generate_dets_n_embs.py --source ./assets/MOT17-mini/train --yolo-model yolov8n.pt yolov8s.pt --reid-model weights/osnet_x0_25_msmt17.pt
# generate MOT challenge format results based on pregenerated detections and embeddings for a specific trackign method
$ python tracking/generate_mot_metrics.py --dets yolov8n --embs osnet_x0_25_msmt17 --tracking-method botsort
# uses TrackEval to generate MOT metrics for the tracking results under ./runs/mot/<dets+embs+tracking-method>
$ python tracking/val.py --benchmark MOT17-mini --dets yolov8n --embs osnet_x0_25_msmt17 --tracking-method botsort

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

# saves dets and embs under ./runs/dets_n_embs separately for each selected yolo and reid model
$ python tracking/generate_dets_n_embs.py --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
$ python tracking/evolve.py --benchmark MOT17-mini --dets yolov8n --embs osnet_x0_25_msmt17 --n-trials 9 --tracking-method botsort

The set of hyperparameters leading to the best HOTA result are written to the tracker's config file.

Custom tracking examples

Detection

import cv2
import numpy as np
from pathlib import Path

from boxmot import DeepOCSORT


tracker = DeepOCSORT(
    model_weights=Path('osnet_x0_25_msmt17.pt'), # which ReID model to use
    device='cuda:0',
    fp16=False,
)

vid = cv2.VideoCapture(0)

while True:
    ret, im = vid.read()

    # substitute by your object detector, output has to be N X (x, y, x, y, conf, cls)
    dets = np.array([[144, 212, 578, 480, 0.82, 0],
                    [425, 281, 576, 472, 0.56, 65]])

    tracker.update(dets, im) # --> M X (x, y, x, y, id, conf, cls, ind)
    tracker.plot_results(im, show_trajectories=True)

    # break on pressing q or space
    cv2.imshow('BoxMOT detection', im)     
    key = cv2.waitKey(1) & 0xFF
    if key == ord(' ') or key == ord('q'):
        break

vid.release()
cv2.destroyAllWindows()

Pose & segmentation

import cv2
import numpy as np
from pathlib import Path

from boxmot import DeepOCSORT


tracker = DeepOCSORT(
    model_weights=Path('osnet_x0_25_msmt17.pt'), # which ReID model to use
    device='cuda:0',
    fp16=True,
)

vid = cv2.VideoCapture(0)

while True:
    ret, im = vid.read()

    keypoints = np.random.rand(2, 17, 3)
    mask = np.random.rand(2, 480, 640)
    # substitute by your object detector, input to tracker has to be N X (x, y, x, y, conf, cls)
    dets = np.array([[144, 212, 578, 480, 0.82, 0],
                    [425, 281, 576, 472, 0.56, 65]])

    tracks = tracker.update(dets, im) # --> M x (x, y, x, y, id, conf, cls, ind)

    # xyxys = tracks[:, 0:4].astype('int') # float64 to int
    # ids = tracks[:, 4].astype('int') # float64 to int
    # confs = tracks[:, 5]
    # clss = tracks[:, 6].astype('int') # float64 to int
    inds = tracks[:, 7].astype('int') # float64 to int

    # in case you have segmentations or poses alongside with your detections you can use
    # the ind variable in order to identify which track is associated to each seg or pose by:
    # masks = masks[inds]
    # keypoints = keypoints[inds]
    # such that you then can: zip(tracks, masks) or zip(tracks, keypoints)

    # break on pressing q or space
    cv2.imshow('BoxMOT segmentation | pose', im)     
    key = cv2.waitKey(1) & 0xFF
    if key == ord(' ') or key == ord('q'):
        break

vid.release()
cv2.destroyAllWindows()

Tiled inference

from sahi import AutoDetectionModel
from sahi.predict import get_sliced_prediction
import cv2
import numpy as np
from pathlib import Path
from boxmot import DeepOCSORT


tracker = DeepOCSORT(
    model_weights=Path('osnet_x0_25_msmt17.pt'), # which ReID model to use
    device='cpu',
    fp16=False,
)

detection_model = AutoDetectionModel.from_pretrained(
    model_type='yolov8',
    model_path='yolov8n.pt',
    confidence_threshold=0.5,
    device="cpu",  # or 'cuda:0'
)

vid = cv2.VideoCapture(0)
color = (0, 0, 255)  # BGR
thickness = 2
fontscale = 0.5

while True:
    ret, im = vid.read()

    # get sliced predictions
    result = get_sliced_prediction(
        im,
        detection_model,
        slice_height=256,
        slice_width=256,
        overlap_height_ratio=0.2,
        overlap_width_ratio=0.2
    )
    num_predictions = len(result.object_prediction_list)
    dets = np.zeros([num_predictions, 6], dtype=np.float32)
    for ind, object_prediction in enumerate(result.object_prediction_list):
        dets[ind, :4] = np.array(object_prediction.bbox.to_xyxy(), dtype=np.float32)
        dets[ind, 4] = object_prediction.score.value
        dets[ind, 5] = object_prediction.category.id

    tracks = tracker.update(dets, im) # --> (x, y, x, y, id, conf, cls, ind)

    tracker.plot_results(im, show_trajectories=True)

    # break on pressing q or space
    cv2.imshow('BoxMOT tiled inference', im)     
    key = cv2.waitKey(1) & 0xFF
    if key == ord(' ') or key == ord('q'):
        break

vid.release()
cv2.destroyAllWindows()

Contributors

Contact

For Yolo tracking bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please send an email to: [email protected]