Spatial Temporal Graph Convolutional Networks (ST-GCN)

A graph convolutional network for skeleton based action recognition.

Introduction

This repository holds the codebase, dataset and models for the paper

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition Sijie Yan, Yuanjun Xiong and Dahua Lin, AAAI 2018.

[Arxiv Preprint]

Prerequisites

Our codebase is based on Python 2.7. There are a few dependencies to run the code. The major libraries we used are

• PyTorch
• NumPy

Data Preparation

We experimented on two skeleton-based action recognition datasts: NTU RGB+D and Kinetics-skeleton.

NTU RGB+D

NTU RGB+D can be downloaded from their website. Only the 3D skeletons(5.8GB) modality is required in our experiments. After that, tools/ntu_gendata.py should be used to build the database for training or evaluation:

tools/ntu_gendata.py --data_path <path to nturgbd>

where the <path to nturgbd> points to the 3D skeletons modality of NTU RGB+D dataset you download, for example data/NTU-RGB-D/nturgbd+d_skeletons.

Kinetics-skeleton

Kinetics is a video-based dataset for action recognition which only provide raw video clips without skeleton data. To obatin the joint locations, we first resized all videos to the resolution of 340x256 and converted the frame rate to 30 fps. Then, we extracted skeletons from each frame in Kinetics by Openpose. The extracted skeleton data we called Kinetics-skeleton(7.5GB) can be directly downloaded from here.

It is highly recommended storing data in the SSD rather than HDD for efficiency.

Testing Pretrained Models

Get trained models

We provided the trained model weithts of our ST-GCN and the baseline model Temporal-Conv[1]. The model weights can be downloaded by running the script

bash tools/get_pretrained_models.sh

The downloaded models will be stored under the ./model.

Evaluation

Once datasets and models ready, we can start the evaluation.

For cross-view evaluation in NTU RGB+D, run

python main.py --config config/st_gcn/nturgbd-cross-view/test.yaml

For cross-subject evaluation in NTU RGB+D, run

python main.py --config config/st_gcn/nturgbd-cross-subject/test.yaml

To evaluate ST-GCN model pretrained on Kinetcis-skeleton, run

python main.py --config config/st_gcn/kinetics-skeleton/test.yaml

Similary, the configuration file for testing baseline models can be found under the ./config/baseline.

To speed up evaluation by multi-gpu inference or modify batch size for reducing memory cost, use

python main.py --config <config file> --test-batch-size <batch size> --device <gpu0> <gpu1> <gpu2> ...

The expected Top-1 accuracy of provided models are shown here:

Model	Kinetics- skeleton (%)	NTU RGB+D Cross View (%)	NTU RGB+D Cross Subject (%)
Baseline	20.3	83.1	74.3
ST-GCN (Ours)	30.6	88.9	80.7

[1] Kim, T. S., and Reiter, A. 2017. Interpretable 3d human action analysis with temporal convolutional networks. In BNMW CVPRW.

Training

To train a new model, run

python main.py --config config/st_gcn/<dataset>/train.yaml

where the <dataset> should be one of nturgbd-cross-view, nturgbd-cross-subject and kinetics-skeleton. The training results will be saved under the ./work_dir by default, including weights, configurations and logging files.

You can modify the training parameters such as batch-size, step, base_lr and device in the command line or configuration files. The order of priority is: command line > config file > default parameter. For more information, use main.py -h.

Finally, custom model evaluation can be achieved by this command:

python main.py --phase test --config <path to training config> --weights <path to model weights>