/ Machine Learning

A Library of Multi-Object Tracking in Python and Pytorch

A Library of Multi-Object Tracking in Python and Pytorch

libmot

A Library of Multi-Object Tracking.

Installation

environments: python 3.6.10, opencv 4.1.1, pytorch 1.3+

git clone https://github.com/nightmaredimple/libmot --recursive
cd libmot/
pip install -r requirements.txt

Feature Lists

Block Method Reference Complete
IOU Assignment iou-tracker&V-IOU
Linear Assignment -
Data Association MinCostFlow MCF
Other End-to-End Network DAN&DeepMOT
GNN&GCN MPNTrack
----------------------- ----------------------------------- ---------------------- ---
Kalman Filter Sort&DeepSort
Motion ECC Tracktor++
Epipolar Geometry TNT
----------------------- ----------------------------------- ---------------------- ---
Re-ID -
Appearance Feature Fusion -
Feature Selection -
----------------------- ----------------------------------- ---------------------- ---
Detection Faster RCNN + FPN Tracktor++
----------------------- ----------------------------------- ---------------------- ---
SOT CF&Siam KCF&CN
----------------------- ----------------------------------- ---------------------- ---
DataLoader -
Tricks Spatial Blocking -
----------------------- ----------------------------------- ---------------------- ---
Evaluation -
Others Tracking Visualiztion -
Feature Visualiztion -
----------------------- ----------------------------------- ---------------------- ---
Tracktor MIFT(ours) -
----------------------- ----------------------------------- ---------------------- ---
Detector MIFD(ours) -

Motion Model

python scripts/test_kalman_tracker.py

3222323-1

Data Association

linear_assignment

Tracktor

Our proposed MIFT and MIFD will be released upon the acceptance on ECCV20'

In MOT Challenge, the MIFT tracktor is named as ISE-MOT, the MIFD detector is named as ISE-MOTDet.

Method DataSets MOTA↑ IDF1↑ MT↑ ML↓ FP↓ FN↓ ID Sw.↓ Frag↓ Hz↑
MOT15 48.1 52.1 29.5% 26.2% 10246 20840 776 1197 6.7
MIFT MOT16 60.4 57.3 24.6% 28.9% 5510 66723 704 932 6.9
MOT17 60.1 56.2 28.1% 27.8% 22265 200077 2644 3206 7.2
Method DataSets AP↑ MODA↑ FAF↓ Precision↑ Recall↑
MIFD MOT17Det 0.88 70.7 4.1 81.4 91.7

GitHub

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