A Sliding Window Scheme for Online Temporal Action Localization (OAT-OSN)

A Sliding Window Scheme for Online Temporal Action Localization (ECCV2022)
Young Hwi Kim, Hyolim Kang, Seon Joo Kim
[link]

Updates

17 Jul, 2022: Initial release

Installation

Prerequisites

• Ubuntu 18.04
• Python 3.8.8
• CUDA 11.0

Requirements

• pytorch==1.8.1
• numpy==1.19.2
• h5py==3.6.0
• ...

To install all required libraries, execute the pip command below.

pip install -r requirement.txt

Training

Input Features

We provide the Kinetics pre-trained feature of THUMOS'14 dataset. The extracted features can be downloaded from link.
Files should be located in 'data/'.
You can also get the feature files from here.

Trained Model

The trained models that used Kinetics pre-trained feature can be downloaded from link.
Files should be located in 'checkpoints/'.

Training Model by own

To train the main OAT model, execute the command below.

python main.py --mode=train

To train the post-processing network (OSN), execute the commands below.

python supnet.py --mode=make --inference_subset=train
python supnet.py --mode=make --inference_subset=test
python supnet.py --mode=train

Testing

To test OAT-OSN, execute the command below.

python main.py --mode=test

To test OAT-NMS, execute the command below.

python main.py --mode=test --pptype=nms

Results

Method	0.3	0.4	0.5	0.6	0.7
OAT-OSN	63.0	56.7	47.1	36.3	20.0
OAT-NMS	69.7	64.0	53.9	42.9	27.0

Citing OAT-OSN

Please cite our paper in your publications if it helps your research:

@inproceedings{kim2022sliding,
  title={A Sliding Window Scheme for Online Temporal Action Localization},
  author={Kim, Young Hwi and Kang, Hyolim and Kim, Seon Joo},
  booktitle={European Conference on Computer Vision},
  pages={653--669},
  year={2022},
  organization={Springer}
}