TGSR

This is the official implementation with training code for “Trajectory Guided Robust Visual Object Tracking with Selective Remedy”.

Introduction

In the paper, we propose a generic, fast and flexible approach to improve the robustness of Siamese trackers with two light-load novel modules: Trajectory Guidance Module (TGM) and Selective Refinement Module (SRM). Specifically, TGM encourages to pay a soft attention on possible target location based on short-term historical trajectory. SRM selectively remedies the tracking results at the risk of failure with little impact on the speed. The proposed algorithm can be easily establish upon state-of-the-art Siamese trackers and obtains better performance on seven benchmarks with high real-time tracking speed.

Installation

Get the code by git clone https://github.com/TJUMMG/TGSR.git.

Alternatively, you can download the zip TGSR.zip in Baidupan, keyword: 9tu5. TGSR.zip has already included raw results, models and pkl results.

You can use the following command to build your environment.

conda create -n verify python=3.7
conda activate verify
conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch

pip install -r requirements.txt 
python setup.py build_ext --inplace
git clone https://github.com/vacancy/PreciseRoIPooling.git

Please refer to PySOT_INSTALL.md and PreciseRoIPooling_README.md to solve the installation problem.

Result

raw result file in Baidupan, keyword: 9tu5

Dataset	Evaluation	SiamRPN++	SiamRPN++_TG	SiamRPN++_SR	SiamRPN++_TGSR
VOT2016	EAO	0.464	0.480	0.486	0.493
VOT2018	EAO	0.415	0.435	0.422	0.440
VOT2019	EAO	0.287	0.292	0.290	0.295
OTB100	AUC	0.696	0.698	0.697	0.698
	Pre	0.905	0.909	0.907	0.914
DTB	AUC	0.614	0.615	0.616	0.624
	Pre	0.800	0.804	0.803	0.814
NFS30	AUC	0.507	0.509	0.518	0.520
	Pre	0.598	0.600	0.612	0.614
LaSOT	AUC	0.497	0.502	0.498	0.502
	NormPre	0.571	0.577	0.573	0.578
	Pre	0.490	0.495	0.491	0.496

Usage

Modify the path

1. modify the path in the python script (e.g., ./tools/test_SiamRPN++_VOT.py)

   sys.path.append('/media/HardDisk_new/wh/TGSR/')   # path to TGSR
   os.system("cd /media/HardDisk_new/wh/TGSR/tools/")	# path to current folder

2. modify the dataset path (e.g., dataset_root in./tools/test_SiamRPN++_VOT.py)

   dataset_root = os.path.join('/media/HardDisk_new/DataSet/test/', args.dataset)  # path to your pysot dataset

Test & Eval

1. download models in Baidupan, keyword: 9tu5

   • experiments.zip : the model of SiamRPN++ and SiamMask, should be unzipped to ./experiments

2. download the TGSR models in Baidupan, keyword: 9tu5

   • snapshot_test.zip : the model of TGSR, should be unzipped to ./snapshot_test

3. run the command

   python ./tools/test_SiamRPN++_VOT.py --dataset VOT2016

4. evaluate the tracker performance

   python ./tools/eval.py --dataset VOT2016

Train

1. run the ./pioneer/traj_predict_train.py to train TPN

2. run the ./pioneer/IoU_train.pyto train IPN

3. run the ./pioneer/Refine_train.py to train BRN

Eval ALTL

1. download the pkl result in Baidupan, keyword: 9tu5

   • research.zip : the model of TGSR, should be unzipped to ./poineer/research/

2. run the command and get the Average Longest Tracking Length (ALTL) of SiamRPN++_TGSR on the VOT2016

   python ./pioneer/research/eval_tool.py
   

Acknowledgments

1. PySOT
2. pytracking
3. PreciseRoIPooling_README.md
4. DR_Loss