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DROL

This is the repo for paper "Discriminative and Robust Online Learning for Siamese Visual Tracking" [paper] [results], presented as poster at AAAI 2020.

Introduction

The proposed Discriminative and Robust Online Learning (DROL) module is designed to work with a variety of off-the-shelf siamese trackers. Our method is extensively evaluated over serveral mainstream benchmarks and is believed to induce a consistant performance gain over the given baseline. The model includes but not limited to, as paper evaluated:

SiamRPN++ (DROL-RPN)
SiamMask (DROL-MASK)
SiamFC (DROL-FC)

Model Zoo

The corresponding offline-trained models are availabe at PySOT Model Zoo.

Get Started

Installation

Please find installation instructions for PyTorch and PySOT in INSTALL.md.
Add DROL to your PYTHONPATH

export PYTHONPATH=/path/to/drol:$PYTHONPATH

Download models

Download models in PySOT Model Zoo and put the model.pth to the corresponding directory in experiment.

Test tracker

cd experiments/siamrpn_r50_l234_dwxcorr
python -u ../../tools/test.py 	\
	--snapshot model.pth 	\ # model path
	--dataset VOT2018 	\ # dataset name
	--config config.yaml	  # config file

Eval tracker

assume still in experiments/siamrpn_r50_l234_dwxcorr_8gpu

python ../../tools/eval.py 	 \
	--tracker_path ./results \ # result path
	--dataset VOT2018        \ # dataset name
	--num 1 		 \ # number thread to eval
	--tracker_prefix 'model'   # tracker_name

Others

For DROL-RPN, we have seperate config file thus each own experiment file folder for vot/votlt/otb/others, where vot is used for VOT-20XX-baseline benchmark, votlt for VOT-20XX-longterm benchmark, otb for OTB2013/15 benchmark, and others is default setting thus for all the other benchmarks, including but not limited to LaSOT/TrackingNet/UAV123.
For DROL-FC/DROL-Mask, only experiments on vot/otb are evaluated as described in the paper. Similar to the repo of PySOT, we use config file for vot as default setting.
Since this repo is a grown-up modification of PySOT, we recommend to refer to PySOT for more technical issues.

References

Jinghao Zhou, Peng Wang, Haoyang Sun, 'Discriminative and Robust Online Learning For Siamese Visual Tracking', Proc. AAAI Conference on Artificial Intelligence (AAAI), 2020.