motrack 0.7.0

Tracking-by-detection (MOT) package

Motrack: Multi-Object Tracking Library

Introduction

Motrack is a versatile multi-object tracking library designed to leverage the tracking-by-detection paradigm. It supports a range of tracker algorithms and object detections, making it ideal for applications in various domains.

Usage

Pseudocode for tracker utilization:

from motrack.object_detection import YOLOv8Inference
from motrack.tracker import ByteTracker, TrackletState

tracker = ByteTracker()  # Default parameters
tracklets = []
yolo = YOLOv8Inference(...)

video_frames = read_video(video_path)

for i, image in enumerate(video_frames):
  detections = yolo.predict_bboxes(image)
  tracklets = tracker.track(tracklets, detections, i)
  active_tracklets = [t for t in tracklets if t.state == TrackletState.ACTIVE]

  foo_bar(active_tracklets)

This library offers flexibility to use any custom object detector.

Implementation of custom tracker:

from typing import List, Tuple

from motrack.library.cv.bbox import PredBBox
from motrack.tracker import Tracker, Tracklet


class MyTracker(Tracker):
  def track(
    self,
    tracklets: List[Tracklet],
    detections: List[PredBBox],
    frame_index: int
  ) -> List[Tracklet]:
    ... Tracker logic ...

    return tracklets

Similarly, custom object detection inference, filter, association method or dataset can also be implemented and seamlessly combined with other components.

Features

Supported tracker algorithms

Method Name	Description
SORT	arxiv: Simple Online and Realtime Tracking
DeepSORT	arxiv: SIMPLE ONLINE AND REALTIME TRACKING WITH A DEEP ASSOCIATION METRIC
MoveSORT	SORT with improved association method
ByteTrack	arxiv: ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Bot-SORT	arxiv: BoT-SORT: Robust Associations Multi-Pedestrian Tracking
SparseTrack	arxiv: SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth

Evaluation of these methods on different datasets can be found in evaluation.md

Supported object detection algorithms

Method Name	Description
YOLOX	arxiv: Simple Online and Realtime Tracking
YOLOv8	github: Ultralytics YOLOv8

Use tools/detection/create_yolo_format.py to create a YOLOv8 training dataset and tools/detection/create_coco_format.py to create a YOLOX training dataset.

FastReID integration

Any FastReID model for appearance matching can be used. Model has to be exported in ONNX. Please check deploy documentation for mode info. Use tools/reid/create_fastreid_patches.py to create a FastReID dataset for training an appearance model.

Supported datasets

Currently supported datasets are: MOT17, MOT20, DanceTrack and SportsMOT.

Any custom dataset can be added by extending the base dataset.

Tools

After installation, three console scripts are available from any directory:

• motrack-inference: Run tracker inference. Outputs are stored under a deterministic hash-based directory.
• motrack-eval: Evaluate tracker outputs with HOTA, CLEAR, Identity, and Count metrics. Results are logged and saved as JSON.
• motrack-optimize: Run Optuna hyperparameter optimization over a tracker's search space.

Equivalent invocations:

uv run motrack-inference                   # console script via uv
uv run python -m motrack.cli.inference     # module run via uv
python tools/inference.py                  # legacy (still works)

If your venv is already activated (source .venv/bin/activate), drop the uv run prefix.

Two additional tools are available as scripts in the repo:

• Postprocess (tools/postprocess.py): Offline postprocessing (linear interpolation, etc.).
• Visualize (tools/visualize_inference.py): Visualize tracker inference.

Library API

The CLI is a thin Hydra wrapper around the library API. Anything the CLI does can be called directly:

from motrack.tools import run_inference, run_eval, run_optimize

run_inference(cfg)                   # build dataset/detector/tracker and run
results = run_eval(cfg)              # HOTA / CLEAR / Identity / Count
run_optimize(cfg)                    # full Optuna study

Dataset construction is pluggable — pass a custom builder if your library wraps a different dataset layer:

from motrack.tools import run_optimize, DatasetBuilder
from motrack.datasets import BaseDataset

def my_dataset_builder(cfg) -> BaseDataset:
    return MyCustomDataset(cfg.dataset.params)

run_optimize(cfg, dataset_builder=my_dataset_builder)

Evaluation

Motrack includes a built-in evaluation module (motrack/eval) that computes HOTA, CLEAR (MOTA/MOTP), Identity (IDF1), and Count metrics — no external TrackEval installation required. Run evaluation after inference:

uv run motrack-eval

Results are saved as eval_results.json inside the tracker run directory.

Evaluation of different supported methods can be found here.

Installation

Motrack is developed and tested with uv. Adding it to a uv-managed project:

uv add motrack

Or installing into the current environment:

uv pip install motrack

Pip works too if you prefer:

pip install motrack

Package page can be found on PyPI.

Working from a clone

git clone https://github.com/Robotmurlock/Motrack.git
cd Motrack
uv sync

This installs all runtime dependencies and registers the motrack-inference, motrack-eval, and motrack-optimize console scripts in the project venv.

Optional extras

Extra	Provides
yolov8	ultralytics (YOLOv8 inference)
reid	onnxruntime (FastReID inference)
optuna	optuna (hyperparameter search)
mlflow	mlflow (experiment tracking)

Install one or more:

uv add 'motrack[yolov8,reid]'
# or
uv pip install 'motrack[yolov8,reid]'
# or (pip)
pip install 'motrack[yolov8,reid]'

For GPU onnxruntime, replace the reid extra with a direct install:

uv add onnxruntime-gpu     # or: pip install onnxruntime-gpu

Changelog

Package changelog can be found here