motrack 0.1.0

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,
    inplace: bool = True
  ) -> 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

• Tracker Algorithms Support:
  • SORT
  • ByteTrack
  • SparseTrack
• Object Detection Inference:
  • YOLOX
  • YOLOv8
• Kalman Filter:
  • Bot-Sort Kalman filter implementation
• Association Methods:
  • IoU (SORT)
  • Move
  • CBIoU
  • DCM
  • And more...
• Dataset Format Support:
  • MOT: MOT17, MOT20, DanceTrack
• Tools:
  • Inference: Perform any tracker inference that can directly evaluated with TrackEval framework.
  • Postprocess: Perform offline postprocessing (linear interpolation, etc...) for more accuracy tracklets.
  • Visualize: Visualize tracker inference.

Installation

Run these commands to install package within your virtual environment or docker container.

git clone https://github.com/Robotmurlock/Motrack
cd Motrack
pip install -e .