DD-Net

(A Double-feature Double-motion Network)

1.About this code

A lightweight network for body/hand action recognition, implemented by keras tensorflow backend. It also could be the simplest tutorial code to start skeleton-based action recognition.

2.How to use this code

(1) create an anaconda environment by following command

conda env create -f=DD-Net_env.yml

(2) go to the folder of JHMDB or SHREC to play with ipython notebooks.

Note: You can download the raw data and use our code to preprocess them, or, directly use our preprocessed data under /data.

JHMDB raw data download link:   http://jhmdb.is.tue.mpg.de/challenge/JHMDB/datasets
SHREC raw data download link:   http://www-rech.telecom-lille.fr/shrec2017-hand/

An alternative choice:

If you do not have enough resource to run this code, please go to use https://colab.research.google.com/drive/19gq3bUigdxIfyMCoWW93YhLEi1KQlBit. We have the preprocessed data under /data, you can download the data and upload them to colab->files, and then run our code on colab.

3.Problems this code try to alleviate

4.Performance

No. parameters	SHREC-14	SHREC-28
1.82 M	94.6	91.9
0.15 M	91.8	90.0

No. parameters	JHMDB
0.50 M	78.0
0.15 M	74.7

Note: if you want to test the speed, please try to run the model.predict() at leat twice and do not take the speed of first run, the model initialization takes extra time.

5.Citation

If you find this code is helpful, thanks for citing our work as,

@online{1907.09658,
Author = {Fan Yang and Sakriani Sakti and Yang Wu and Satoshi Nakamura},
Title = {Make Skeleton-based Action Recognition Model Smaller, Faster and Better},
Year = {2019},
Eprint = {1907.09658},
Eprinttype = {arXiv},
}

Hey, come take a look

ngiraffe/DD-Net