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

• 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 setup options that meet different hardware limitations: CPU only, low memory GPUs... Everything is designed with simplicity and flexibility in mind. If you don't get good tracking results on your custom dataset with the out-of-the-box tracker configurations, use the examples/evolve.py script for tracker hyperparameter tuning.

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 -v -e .

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

Tracking methods

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

Tracking sources

Tracking can be run on most video formats

$ python examples/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 examples/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 examples/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

MOT compliant results

Can be saved to your experiment folder runs/track/exp*/ by

python examples/track.py --source ... --save-mot

Evaluation

Evaluate a combination of detector, tracking method and ReID model on standard MOT dataset or you custom one by

$ python3 examples/val.py --yolo-model yolo_nas_s.pt --reid-model osnetx1_0_dukemtcereid.pt --tracking-method deepocsort --benchmark MOT16
                          --yolo-model yolox_n.pt    --reid-model osnet_ain_x1_0_msmt17.pt  --tracking-method ocsort     --benchmark MOT17
                          --yolo-model yolov8s.pt    --reid-model lmbn_n_market.pt          --tracking-method strongsort --benchmark <your-custom-dataset>

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

$ python examples/evolve.py --tracking-method strongsort --benchmark MOT17 --n-trials 100  # tune strongsort for MOT17
                            --tracking-method ocsort     --benchmark <your-custom-dataset> --objective HOTA # tune ocsort for maximizing HOTA on your custom tracking dataset

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

Custom object detection model example

Minimalistic

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]])

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

Complete

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)
color = (0, 0, 255)  # BGR
thickness = 2
fontscale = 0.5

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

    # 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) # --> (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:
    # segs = segs[inds]
    # poses = poses[inds]
    # you can then zip them together: zip(tracks, poses)

    # print bboxes with their associated id, cls and conf
    if tracks.shape[0] != 0:
        for xyxy, id, conf, cls in zip(xyxys, ids, confs, clss):
            im = cv2.rectangle(
                im,
                (xyxy[0], xyxy[1]),
                (xyxy[2], xyxy[3]),
                color,
                thickness
            )
            cv2.putText(
                im,
                f'id: {id}, conf: {conf}, c: {cls}',
                (xyxy[0], xyxy[1]-10),
                cv2.FONT_HERSHEY_SIMPLEX,
                fontscale,
                color,
                thickness
            )

    # show image with bboxes, ids, classes and confidences
    cv2.imshow('frame', im)

    # break on pressing q
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

vid.release()
cv2.destroyAllWindows()

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]