IIP_TwoS_Video_Saliency

It is a re-implementation code for the following paper:

Kao Zhang, Zhenzhong Chen. Video Saliency Prediction Based on Spatial-Temporal Two-Stream Network. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), vol. 29, no. 12, pp. 3544-3557, 2019.
[Online] Avaliable: https://ieeexplore.ieee.org/document/8543830

Installation

Environment:

The code was developed using Python 3.6 & Keras 2.2.4 & CUDA 9.0. There may be a problem related to software versions.To fix the problem, you may look at the implementation in "zk_models.py" file and replace the syntax to match the new keras environment.

• Windows10/Ubuntu16.04
• Anaconda 5.2.0
• Python 3.6
• CUDA 9.0 and cudnn7.1.2

Pre-trained models

Download the pre-trained models and put the pre-trained model into the "Models" file.

• TwoS-model Baidu Drive; Google Drive; OneDrive (210M)

• SF-Net-model Baidu Drive; Google Drive; OneDrive (58M)

Python requirements

Currently, the code supports python 3.6

• conda
• Keras ( >= 2.2.4)
• tensorflow ( >= 1.12.0)
• python-opencv
• hdf5storage

Train and Test

• please change the working directory: "wkdir" to your path in the "zk_config.py" file, like

  dataDir = 'E:/Code/IIP_TwoS_Saliency/DataSet'

• More parameters are in the "zk_config.py" file.

• Run the demo "Test_TwoS_Net.py" and "Train_TwoS_Net.py" to test or train the model.

The full training process:

Our model is trained on SALICON and part of the DIEM dataset. We train the SF-Net in spatial stream based on the pre-trained VGG-16 model and the training set of SALICON dataset. Then, we train the whole network on the training set of DIEM dataset, and fix the parameters of the trained SF-Net.

• Please download SALICON and DIEM dataset.
• Run the demo "Train_Test_ST_Net.py" to get pre-trained SF-Net model.
• Run the demo "Train_TwoS_Net.py" to train the whole model.

Output format

And it is easy to change the output format in our code.

• The results of video task is saved by ".mat"(uint8) formats.
• You can get the color visualization results based on the "Visualization Tools".
• You can evaluate the performance based on the "EvalScores Tools".

Paper & Citation

If you use the TwoS video saliency model, please cite the following paper:

@article{Zhang2018Video,
  author  = {Kao Zhang and Zhenzhong Chen},
  title   = {Video Saliency Prediction Based on Spatial-Temporal Two-Stream Network},
  journal = {IEEE Transactions on Circuits and Systems for Video Technology },
  year    = {2018}
}

Contact

Kao ZHANG
Laboratory of Intelligent Information Processing (LabIIP)
Wuhan University, Wuhan, China.
Email: zhangkao@whu.edu.cn

Zhenzhong CHEN (Professor and Director)
Laboratory of Intelligent Information Processing (LabIIP)
Wuhan University, Wuhan, China.
Email: zzchen@whu.edu.cn
Web: http://iip.whu.edu.cn/~zzchen/