DCHT: Dense Convolutional Histogram-based Tracking

Tracking with histograms of convolutional features.

Tested on Python 3.7, Install requirements as:

pip install -r requirements.txt

Pretrained models

A pretrained model is available achieving the following performance on OTB2015:

Backbone weights pretrained on ImageNet are also available as starting weights when training a new model.

Training

Training is handled by the train.py script:

python train.py -h

usage: train.py [-h] [--epochs EPOCHS] [--batch-size BATCH_SIZE] [--num-workers NUM_WORKERS] [--log-freq LOG_FREQ] [--device DEVICE] [--lr LR] [--weight-decay WEIGHT_DECAY] [--data-root DATA_ROOT]

optional arguments:
  -h, --help            show this help message and exit
  --epochs EPOCHS       Number of training epochs.
  --batch-size BATCH_SIZE
                        Batch size for training.
  --num-workers NUM_WORKERS
                        Number of threads used for data loading.
  --log-freq LOG_FREQ   Logging frequency (iterations)
  --device DEVICE       Device used for training
  --lr LR               Initial learning rate.
  --weight-decay WEIGHT_DECAY
                        Weight decay rate.
  --data-root DATA_ROOT
                        Dataset root path.

By default, the COCO detection dataset is used for training, but the following datasets are available as training sets:

• COCO Detection
• TrackingNet
• ImageNet VID
• VisDrone2018SOT
• VisDrone2018MOT

Inference

The following datasets are available for testing:

• VOT2014, VOT2015, VOT2016, VOT2017
• UAV123
• OTB2015 [download + preparation]

The validation set from CFNet is also supported via the VOT dataset class.

Testing is handled by the test.py script:

python test.py -h

usage: test.py [-h] [--device DEVICE] [--weights WEIGHTS] [--data-type {uav123,uav20l,vot,otb}] [--data-root DATA_ROOT] [--show] [--write-mat]

optional arguments:
  -h, --help            show this help message and exit
  --device DEVICE       Device used for testing.
  --weights WEIGHTS     Path to model weights.
  --data-type {uav123,uav20l,vot,otb}
                        Dataset type.
  --data-root DATA_ROOT
                        Dataset root path.
  --show                Visualize result.
  --write-mat           Write result to mat file to be read by benchmark toolkits.

The result can be saved to .mat files to be used in benchmark toolkits for comparison with other trackers. IMPORTANT: the evaluation performed by this code as notable differences to the evaluation performed by the OTB/UAV123 MATLAB toolkit and the results are mostly indicative.

Citation

@article{nousi2020dense,
  title={Dense convolutional feature histograms for robust visual object tracking},
  author={Nousi, Paraskevi and Tefas, Anastasios and Pitas, Ioannis},
  journal={Image and Vision Computing},
  volume={99},
  pages={103933},
  year={2020},
  publisher={Elsevier}
}