Release v10.0.13

• Inputs (det, im0) to all trackers are now numpy.ndarray
• Fix README custom model example
• Clean up classifiers in setup.py
• Add trackers instantiation pytest tests
• Lower CI time by ~2 minutes by using a more CPU friendly architecture

Release v10.0.12

• Fix google colab example
• Less verbose evaluation
• The evaluation process stops if a single subprocess fails.

Release v10.0.11

• Minimal requirements
• Avoid ultralytics installation by default, only if Yolov8 model is used for tracking
• Avoid SG installation by default, only if YOLO-NAS is used for tracking
• Update OpenVINO export
• 33% CI speedup by lower imgsz resolution
• Boxmot paths imports. From:

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0].parents[0]  # repo root absolute path
EXAMPLES = FILE.parents[0]  # examples absolute path
WEIGHTS = EXAMPLES / 'weights'

in each example file, to:

from boxmot.utils import ROOT, EXAMPLES, WEIGHTS

Release v10.0.10

• configure non-yolov8 models for inference, based on input arguments
• add @torch.no_grad() to all ReID related inference methods

Release v10.0.9

• Multi Yolo backend added (Ultralytics + SuperGradients)
• Unified yolo-nas, yolox and yolov8 tracking file
• • python examples/track.py --yolo-model yolo_nas_s- - python examples/track.py --yolo-model yolo_nas_s
• All YOLO models tested in CI

Release v10.0.8

• Simultaneous pypi + release publishing in CI

v10.0.6

• Fix conf not getting updated in DeepOCSORT: #877
• Add comments to custom object detector example
• Fixed example/track.py Yolov8 bug. Filter out detections, sementation masks and pose estimations based on tracking results

v.10.0.4

• Complete repo refactor
• PyPI package published
• Yolo-NAS example added

v10.0.0

• Tracking now available for all main computer vision tasks:
  - detection
  - segmentation
  - pose estimation
• Added DeepOCSORT to trackers
• Added LightMBN to ReID models
• Added ReID export compatibility for --half with --dynamic, thanks to @Rm1n90 in #798
• ReID TFlite model export now available using onnx2tf instead of using openvino2tensorflow
• Deleted Yolov8 submodule in favour of a pure Yolov8 package implementation
• Complete track.py refactor
• Simplified val.py
• Log val and evolve results to tensorboard. Usage
  • tensorboard --logdir=./runs/evolve/
  • tensorboard --logdir=./runs/val/

v9.0 - Mult-object tracking and segmentation, Multi-objective genetic algorithm for custom dataset tracker tuning, Yolov5 to Yolo8 upgrade

The goal of this release is to add segmentation tracking capability and specific tracking method hyperparameter tuning on MOTXX & custom tracking datasets.

Important updates

• Tracker hyperparameter tuning using "A fast and elitist multiobjective genetic algorithm: NSGA-II", based on custom dataset evaluation results using Optuna (https://ieeexplore.ieee.org/document/996017)
• Upgraded Yolov5 to Yolov8
• Segmentation tracking available by loading segmentation Yolov8 models: python --yolo-weight yolov8m-seg.pt
• Added Dockerfile
• Added CITATION.cff
• MOT17 toy dataset created for CI purposes
• Trajectories plotting:
  • class color for matched observations
  • White for unmatched predictions
  • Activate it using the --save-trajectories flag
• val.py working on Windows (#624)
• Confidences now handled internally in each tracker
• Actions update in CI pipeline:
  • actions/checkout@v2 --> v3
  • actions/setup-python@v3 --> v4
• val.py and evolve.py added to CI CPU pipeline
• Experiment results:
  • Segmentation model vs object detection model on MOT metrics
  • Masked detections vs regular detection for feature extraction
  • conf-thres vs main MOT metrics (HOTA, MOTA, IDF1)
  • Optimal max number of StrongSORT preds ahead & updates for for MOT17 with current eval setting

MOT17-train evaluation results

From now on we will evaluate our tracking results using COCO trained models only. Yolo M will still be the standard model. This is so that we can focus on tracking as we don't have enough resources for a broader scope for this repo.

HOTA: MOT17-pedestrian             HOTA      DetA      AssA      DetRe     DetPr     AssRe     AssPr     LocA      OWTA      HOTA(0)   LocA(0)   HOTALocA(0)
MOT17-02-FRCNN                     33.206    24.564    45.1      25.163    84.839    46.954    86.804    86.686    33.647    38.14     83.737    31.938    
MOT17-04-FRCNN                     41.731    32.692    53.308    33.877    84.349    56.459    86.095    86.814    42.495    50.444    82.724    41.729    
MOT17-05-FRCNN                     42.656    41.307    44.419    45.386    69.872    51.719    68.892    78.993    44.857    59.422    71.221    42.321    
MOT17-09-FRCNN                     55.857    57.727    54.145    62.213    80.136    57.66     82.182    84.264    58.037    71.027    79.983    56.81     
MOT17-10-FRCNN                     45.976    43.936    48.289    47.112    74.19     51.34     78.774    80.124    47.684    61.839    74.3      45.946    
MOT17-11-FRCNN                     59.008    52.593    66.413    59.229    76.382    72.324    84.023    85.503    62.712    74.143    79.733    59.116    
MOT17-13-FRCNN                     39.175    32.501    47.443    34.011    77.744    51.174    78.836    82.474    40.139    49.312    78.08     38.503    
COMBINED                           43.582    36.14     52.761    38.145    79.618    56.557    83.461    84.453    44.84     54.209    79.569    43.133    

CLEAR: MOT17-pedestrian            MOTA      MOTP      MODA      CLR_Re    CLR_Pr    MTR       PTR       MLR       sMOTA     CLR_TP    CLR_FN    CLR_FP    IDSW      MT        PT        ML        Frag      
MOT17-02-FRCNN                     27.84     84.61     28.045    28.852    97.278    11.29     30.645    58.065    23.4      5361      13220     150       38        7         19        36        155       
MOT17-04-FRCNN                     34.552    85.785    34.657    37.41     93.147    12.048    39.759    48.193    29.234    17791     29766     1309      50        10        33        40        345       
MOT17-05-FRCNN                     45.713    75.681    46.451    55.703    85.756    17.293    63.158    19.549    32.167    3853      3064      640       51        23        84        26        186       
MOT17-09-FRCNN                     66.423    82.262    67.08     72.357    93.203    50        46.154    3.8462    53.588    3853      1472      281       35        13        12        1         61        
MOT17-10-FRCNN                     51.39     77.087    51.834    57.668    90.813    26.316    45.614    28.07     38.177    7404      5435      749       57        15        26        16        277       
MOT17-11-FRCNN                     52.522    84.907    52.745    65.144    84.01     32        26.667    41.333    42.69     6147      3289      1170      21        24        20        31        50        
MOT17-13-FRCNN                     38.301    79.725    38.782    41.264    94.326    19.091    36.364    44.545    29.934    4804      6838      289       56        21        40        49        165       
COMBINED                           39.464    82.58     39.738    43.824    91.472    20.696    42.857    36.447    31.83     49213     63084     4588      308       113       234       199       1239      

Identity: MOT17-pedestrian         IDF1      IDR       IDP       IDTP      IDFN      IDFP      
MOT17-02-FRCNN                     38.345    24.859    83.814    4619      13962     892       
MOT17-04-FRCNN                     48.31     33.856    84.298    16101     31456     2999      
MOT17-05-FRCNN                     58.983    48.648    74.894    3365      3552      1128      
MOT17-09-FRCNN                     70.24     62.385    80.358    3322      2003      812       
MOT17-10-FRCNN                     60.785    49.692    78.253    6380      6459      1773      
MOT17-11-FRCNN                     68.644    60.937    78.584    5750      3686      1567      
MOT17-13-FRCNN                     49.824    35.81     81.857    4169      7473      924       
COMBINED                           52.627    38.92     81.236    43706     68591     10095     

Count: MOT17-pedestrian            Dets      GT_Dets   IDs       GT_IDs    
MOT17-02-FRCNN                     5511      18581     72        62        
MOT17-04-FRCNN                     19100     47557     97        83        
MOT17-05-FRCNN                     4493      6917      135       133       
MOT17-09-FRCNN                     4134      5325      58        26        
MOT17-10-FRCNN                     8153      12839     110       57        
MOT17-11-FRCNN                     7317      9436      96        75        
MOT17-13-FRCNN                     5093      11642     115       110       
COMBINED                           53801     112297    683       546