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.

Ackowledgement

• pysot
• pytracking