- CVPR 2019 version.
- Full version (with the appendix).
NOTE:
- A new implementation (with pytorch 1.1) will soon be available.
- Recently the DukeMTMC website was disabled and would hopefully be recovered in the future.
Click it to watch longer ↑
Left: input. Middle: reconstruction. Right: memory (Row 1), attention (Row 2), and output (Row 3).
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 |
Click it to watch longer ↑
Left: input. Middle: reconstruction. Right: memory (Row 1), attention (Row 2), and output (Row 3).
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 |
Click it to watch longer ↑
Rows 1 and 4: input. Row 2 and 5: reconstruction. Rows 3 and 6: output.
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 hosted at https://motchallenge.net/results/DukeMTMCT, where our TBA tracker is named as ‘MOT_TBA’.
- python 3.6
- pytorch 0.3.1
- py-motmetrics (to evaluate tracking performances)
Enter the project root directory cd path/to/tba
.
For mnist and sprite:
python scripts/gen_mnist.py # for mnist
python scripts/gen_sprite.py # for sprite
For duke:
bash scripts/mts2jpg.sh 1 # convert .mts files to .jpg files, please run over all cameras by setting the last argument to 1, 2, ..., 8
./scripts/build_imbs.sh # build imbs for background extraction
cd imbs/build
./imbs -c 1 # run imbs, please run over all cameras by setting c = 1, 2, ..., 8
cd ../..
python scripts/gen_duke_bb.py --c 1 # generate bounding box masks, please run over all cameras by setting c = 1, 2, ..., 8
python scripts/gen_duke_bb_bg.py --c 1 # refine background images, please run over all cameras by setting c = 1, 2, ..., 8
python scripts/gen_duke_roi.py # generate roi masks
python scripts/gen_duke_processed.py --c 1 # resize images, please run over all cameras by setting c = 1, 2, ..., 8
python scripts/gen_duke.py # generate .pt files for training
python run.py --task mnist # for mnist
python run.py --task sprite # for sprite
python run.py --task duke # for duke
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
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
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
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
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.