/PGCN

Graph Convolutional Networks for Temporal Action Localization (ICCV2019)

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Graph Convolutional Networks for Temporal Action Localization

This repo holds the codes and models for the PGCN framework presented on ICCV 2019

Graph Convolutional Networks for Temporal Action Localization Runhao Zeng*, Wenbing Huang*, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan, ICCV 2019, Seoul, Korea.

[Paper]

Updates

20/12/2019 We have uploaded the RGB features, trained models and evaluation results! We found that increasing the number of proposals to 800 in the testing further boosts the performance on THUMOS14. We have also updated the proposal list.

04/07/2020 We have uploaded the I3D features on Anet, the training configurations files in data/dataset_cfg.yaml and the proposal lists for Anet.

Contents

Usage Guide

Prerequisites

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The training and testing in PGCN is reimplemented in PyTorch for the ease of use.

Other minor Python modules can be installed by running

pip install -r requirements.txt

Code and Data Preparation

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Get the code

Clone this repo with git, please remember to use --recursive

git clone --recursive https://github.com/Alvin-Zeng/PGCN

Download Datasets

We support experimenting with two publicly available datasets for temporal action detection: THUMOS14 & ActivityNet v1.3. Here are some steps to download these two datasets.

  • THUMOS14: We need the validation videos for training and testing videos for testing. You can download them from the THUMOS14 challenge website.
  • ActivityNet v1.3: this dataset is provided in the form of YouTube URL list. You can use the official ActivityNet downloader to download videos from the YouTube.

Download Features

Here, we provide the I3D features (RGB+Flow) for training and testing.

THUMOS14: You can download it from Google Cloud or Baidu Cloud.

Anet: You can download the I3D Flow features from Baidu Cloud (password: jbsa) and the I3D RGB features from Google Cloud (Note: set the interval to 16 in ops/I3D_Pooling_Anet.py when training with RGB features)

Download Proposal Lists (ActivityNet)

Here, we provide the proposal lists for ActivityNet 1.3. You can download them from Google Cloud

Training PGCN

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Plesse first set the path of features in data/dataset_cfg.yaml

train_ft_path: $PATH_OF_TRAINING_FEATURES
test_ft_path: $PATH_OF_TESTING_FEATURES

Then, you can use the following commands to train PGCN

python pgcn_train.py thumos14 --snapshot_pre $PATH_TO_SAVE_MODEL

After training, there will be a checkpoint file whose name contains the information about dataset and the number of epoch. This checkpoint file contains the trained model weights and can be used for testing.

Testing Trained Models

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You can obtain the detection scores by running

sh test.sh TRAINING_CHECKPOINT

Here, TRAINING_CHECKPOINT denotes for the trained model. This script will report the detection performance in terms of mean average precision at different IoU thresholds.

The trained models and evaluation results are put in the "results" folder.

You can obtain the two-stream results on THUMOS14 by running

sh test_two_stream.sh

THUMOS14

mAP@0.5IoU (%) RGB Flow RGB+Flow
P-GCN (I3D) 37.23 47.42 49.07 (49.64)

#####Here, 49.64% is obtained by setting the combination weights to Flow:RGB=1.2:1 and nms threshold to 0.32

Other Info

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Citation

Please cite the following paper if you feel PGCN useful to your research

@inproceedings{PGCN2019ICCV,
  author    = {Runhao Zeng and
               Wenbing Huang and
               Mingkui Tan and
               Yu Rong and
               Peilin Zhao and
               Junzhou Huang and
               Chuang Gan},
  title     = {Graph Convolutional Networks for Temporal Action Localization},
  booktitle   = {ICCV},
  year      = {2019},
}

Contact

For any question, please file an issue or contact

Runhao Zeng: runhaozeng.cs@gmail.com