thuyngch / libmot

A Library of Multi-Object Tracking

A Library of Multi-Object Tracking in Python and Pytorch

Installation

environments: python 3.6.10, opencv 4.1.1, pytorch 1.3+

git clone https://github.com/nightmaredimple/libmot --recursive
cd libmot/
python setup.py install develop --user

The details can be seen from my blogs or zhihu.

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&Selection	-	☐
	DAN	DAN	✓
-----------------------	-----------------------------------	----------------------	---
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

Data Association

Tracktor

Our proposed MIFT and MIFD will be released upon acceptance.

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

To be continued..