Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

CVPR 2019 version with its appendix
arxiv version

1. Results

1.1 MNIST-MOT

a) Qualitative results

Click it to watch longer ↑
Left: input. Middle: reconstruction. Right: memory (Row 1), attention (Row 2), and output (Row 3).

b) Quantitative results

Configuration	IDF1↑	IDP↑	IDR↑	MOTA↑	MOTP↑	FAF↓	MT↓	ML↓	FP↓	FN↓	IDS↓	Frag↓
TBA	99.6	99.6	99.6	99.5	78.4	0	978	0	49	49	22	7

1.2 Sprites-MOT

a) Qualitative results

Click it to watch longer ↑
Left: input. Middle: reconstruction. Right: memory (Row 1), attention (Row 2), and output (Row 3).

b) Quantitative results

Configuration	IDF1↑	IDP↑	IDR↑	MOTA↑	MOTP↑	FAF↓	MT↓	ML↓	FP↓	FN↓	IDS↓	Frag↓
TBA	99.2	99.3	99.2	99.2	79.1	0.01	985	1	60	80	30	22

1.3 DukeMTMC

a) Qualitative results

Click it to watch longer ↑
Rows 1 and 4: input. Row 2 and 5: reconstruction. Rows 3 and 6: output.

b) Quantitative results

Configuration	IDF1↑	IDP↑	IDR↑	MOTA↑	MOTP↑	FAF↓	MT↓	ML↓	FP↓	FN↓	IDS↓	Frag↓
TBA	82.4	86.1	79.0	79.6	80.4	0.09	1,026	46	64,002	151,483	875	1,481

Quantitative results are made public at https://motchallenge.net/results/DukeMTMCT ('MOT_TBA' with paper ID 648).

2. Requirements

python 3.6
pytorch 0.3.1
py-motmetrics (to evaluate tracking performances)

3. Usage

3.1 Generate training data

cd path/to/tba                  # enter the project root directory
python scripts/gen_mnist.py     # for mnist
python scripts/gen_sprite.py    # for sprite
python scripts/gen_duke.py      # for duke

3.2 Train the model

python run.py --task mnist     # for mnist
python run.py --task sprite    # for sprite
python run.py --task duke      # for duke

3.3 Show training curves

python scripts/show_curve.py --task mnist     # for mnist
python scripts/show_curve.py --task sprite    # for sprite
python scripts/show_curve.py --task duke      # for duke

3.4 Evaluate tracking performances

a) Generate test data

python scripts/gen_mnist.py --metric 1         # for mnist
python scripts/gen_sprite.py --metric 1        # for sprite
python scripts/gen_duke.py --metric 1 --c 1    # for duke, please run over all cameras by setting c = 1, 2, ..., 8

b) Generate tracking results

python run.py --init sp_latest.pt --metric 1 --task mnist                     # for mnist
python run.py --init sp_latest.pt --metric 1 --task sprite                    # for sprite
python run.py --init sp_latest.pt --metric 1 --task duke --subtask camera1    # for duke, please run all subtasks from camera1 to camera8

c) Convert the results into `.txt`

python scripts/get_metric_txt.py --task mnist                     # for mnist
python scripts/get_metric_txt.py --task sprite                    # for sprite
python scripts/get_metric_txt.py --task duke --subtask camera1    # for duke, please run all subtasks from camera1 to camera8

d) Evaluate tracking performances

python -m motmetrics.apps.eval_motchallenge data/mnist/pt result/mnist/tba/default/metric --solver lap      # form mnist
python -m motmetrics.apps.eval_motchallenge data/sprite/pt result/sprite/tba/default/metric --solver lap    # form sprite

To evaluate duke, please upload the file duke.txt (under result/duke/tba/default/metric/) to https://motchallenge.net.

cuimengjie / tracking-by-animation Goto Github PK

tracking-by-animation's Introduction

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

1. Results

1.1 MNIST-MOT

a) Qualitative results

b) Quantitative results

1.2 Sprites-MOT

a) Qualitative results

b) Quantitative results

1.3 DukeMTMC

a) Qualitative results

b) Quantitative results

2. Requirements

3. Usage

3.1 Generate training data

3.2 Train the model

3.3 Show training curves

3.4 Evaluate tracking performances

a) Generate test data

b) Generate tracking results

c) Convert the results into .txt

d) Evaluate tracking performances

tracking-by-animation's People

Contributors

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c) Convert the results into `.txt`