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`Skimming-Perusal' Tracking: A Framework for Real-Time and Robust Long-term Tracking

splt

GOOD NEWS ! ! ! Our code has supported python3.6

Welcome to try(checkout to py36 branch) if you want to run our tracker with python3.6.

This is the official code for the ICCV 2019 paper[arxiv]. This code has been tested on

Please cite our paper if you find it useful for your research.

@inproceedings{ iccv19_SPLT,
    title={`Skimming-Perusal' Tracking: A Framework for Real-Time and Robust Long-term Tracking},
    author={Yan, Bin and Zhao, Haojie and Wang, Dong and Lu, Huchuan and Yang, Xiaoyun},
    booktitle={IEEE International Conference on Computer Vision (ICCV)},
    year={2019}
}

Installation

conda create -n SPLT python=2.7
conda activate SPLT
git clone https://github.com/iiau-tracker/SPLT.git
cd <path/to/SPLT>
pip install -r requirements.txt
conda install cudatoolkit=10.0
conda install cudnn=7.6.0

# or CUDA 9.0 + cuDNN 7.1.2 for TensorFlow  < 1.13.0
conda install cudatoolkit=9.0
conda install cudnn=7.1.2

Models

ModelSizeGoogle DriveBaidu
SiamRPN215 MBmodel.ckpt-470277Mirror
Verifier178 MBV_resnet50_VID_N-65624Mirror
Skimming24 MBSkimMirror

Demo

# modify 'PROJECT_PATH' in 'demo.py' 
python demo.py

Evaluation on VOT

start from RPN_Verifier_Skim_top3.py

raw resluts (vot-toolkt version 6.0.3)

Train

Train the Verifier(optional)

Download ResNet50 model pretrained on IMAGENET.Then put extracted ckpt file in train_Verifier/lib

cd train_Verifier/experiments
# modify paths in classify.py
python classify.py
# modify paths in triplet_pairs.py
python triplet_pairs.py
# modify paths in train_multi_gpu.py
python train_multi_gpu.py

Train the Skimming(optional)

cd train_Skim
# modify paths in classify.py
python classify.py
# modify paths in skim_data.py
python skim_data.py
# modify paths in train_skim.py
python train_skim.py