• 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 

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) 

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

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.

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()

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()

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()