Participation-Contributed Temporal Dynamic Model for Group Activity Recognition. PDF

We give a general DMS(Data, Model, Solver) code framework for PCTDM, impelemented by Pytorch. You can apply new model or dataset into this framework by modifying the files in Configs easily! For further information about me, welcome to my homepage.

Requirements

> Ubuntu 16.04
> pytorch 0.4.1
= python 2.7
pip install dlib

The general piplines of GAR

You can run python GAR.py to excute all the following steps.

Step Zero: Preprocessing dataset

• To download VD and CAD at './dataset/VD' and './dataset/CAD' folder;
• Add none.jpg
• To track the persons and generate the train/test files by using Processing.py;

Step One: Action Level

• To create a Piplines instance as:

  Action = Action_Level(dataset_root, dataset_name, 'trainval_action');

• For action recognition, you can use Action.trainval();
• For extracting action features, you can use Action.extract_feas(save_folder='*').

Step Two: Activity Level

This is the core part of GAR which need to be designed by youself. We proposed a novel PCTDM to aggreate the action features with attending to key persons.

• To create a Piplines instance as:

  Activity = Activity_Level(dataset_root, dataset_name, 'trainval_activity');

• For activity recognition, you can use Activity.trainval().

Step Three: Evaluate

• To show some results at activity level, you can use Activity.evaluate().

All steps may take about 15 hours for 'VD', and 5 hours for 'CAD'.

License and Citation

Please cite the following paper in your publications if it helps your research.

@inproceedings{yan2018participation,
    title={Participation-Contributed Temporal Dynamic Model for Group Activity Recognition},
    author={Yan, Rui and Tang, Jinhui and Shu, Xiangbo and Li, Zechao and Tian, Qi},
    booktitle={2018 ACM Multimedia Conference on Multimedia Conference},
    pages={1292--1300},
    year={2018},
    organization={ACM}
}

Contact Information

Feel free to create a pull request or contact me by Email = ["ruiyan", at, "njust", dot, "edu", dot, "cn"], if you find any bugs.