Don't Forget the Past, Learn From It for Object Tracking

This is the official implementation of the paper: Don't Forget the Past, Learn From It for Object Tracking.

Setup

• Prepare Anaconda, CUDA and the corresponding toolkits. CUDA version required: 10.0+

• Create a new conda environment and activate it.

conda create -n STCTrack python=3.7 -y
conda activate STCTrack

• Install pytorch and torchvision.

conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch
# pytorch v1.5.0, v1.6.0, or higher should also be OK. 

• Install other required packages.

pip install -r requirements.txt

Test datasets format

• Prepare the datasets: OTB2015, VOT2018, UAV123, GOT-10k, TrackingNet, LaSOT, ILSVRC VID*, ILSVRC DET*, COCO*, and something else you want to test. Set the paths as the following:

├── STCTrack
|   ├── ...
|   ├── ...
|   ├── datasets
|   |   ├── COCO -> /opt/data/COCO
|   |   ├── GOT-10k -> /opt/data/GOT-10k
|   |   ├── ILSVRC2015 -> /opt/data/ILSVRC2015
|   |   ├── LaSOT -> /opt/data/LaSOT/LaSOTBenchmark
|   |   ├── OTB
|   |   |   └── OTB2015 -> /opt/data/OTB2015
|   |   ├── TrackingNet -> /opt/data/TrackingNet
|   |   ├── UAV123 -> /opt/data/UAV123/UAV123
|   |   ├── VOT
|   |   |   ├── vot2018
|   |   |   |   ├── VOT2018 -> /opt/data/VOT2018
|   |   |   |   └── VOT2018.json

• Notes

i. Star notation(*): just for training. You can ignore these datasets if you just want to test the tracker.

ii. In this case, we create soft links for every dataset. The real storage location of all datasets is /data/dir/. You can change them according to your situation.

iii. The VOT2018.json file can be download from here.

• Download the models we trained.

  • GOT-10k model (😁Coming soon...)
  • fulldata model (😁Coming soon...)

• Use the path of the trained model to set the pretrain_model_path item in the configuration file correctly, then run the shell command.

• Note that all paths we used here are relative, not absolute. See any configuration file in the experiments directory for examples and details.

General command format

python main/test.py --config testing_dataset_config_file_path

Take GOT-10k as an example:

python main/test.py --config experiments/stctrack/test/got10k/stctrack-got.yaml

Training

• Prepare the datasets as described in the last subsection.
• Download the pretrained backbone model from here( 😁Coming soon...).
• Run the shell command.

training based on the GOT-10k benchmark

python main/train.py --config experiments/stctrack/train/got10k/stctrack-trn.yaml

training with full data

python main/train.py --config experiments/stmtrack/train/fulldata/stctrack-trn-fulldata.yaml

Testing Results

Click Baidu Web Drive(code:0fzv) to download all the following.

• OTB2015: Baidu Web Drive code:z856 , Google Drive(😁Coming soon...)
• GOT-10k:Baidu Web Drive code:xnwq , Google Drive(😁Coming soon...)
• LaSOT:Baidu Web Drive code:6zfu , Google Drive(😁Coming soon...)
• TrackingNet:Baidu Web Drive code:26at , Google Drive(😁Coming soon...)
• UAV123:Baidu Web Drive code:k3id , Google Drive(😁Coming soon...)

Acknowledgement

Repository

• video_analyst
• mmdetection
• pytracking
• PySOT
• Synchronized-BatchNorm-PyTorch

This repository is developed based on the single object tracking framework video_analyst. See it for more instructions and details.

References

None

Contact

• Kai Huang@kevoen

If you have any questions, just create issues or email me:smile:.