README.md

Quasi-Online Detection of Take and Release Actions from Egocentric Videos

Rosario Scavo, Francesco Ragusa, Antonino Furnari, Giovanni Maria Farinella

Code for the Paper: Quasi-Online Detection of Take and Release Actions from Egocentric Videos. International Conference on Image Analysis and Processing 2023.

Find the original paper here.

Citations

If you use the data, code, or model provided here in your publication, please cite our paper.

@inproceedings{scavo2023quasi,
  title={Quasi-Online Detection of Take and Release Actions from Egocentric Videos},
  author={Scavo, Rosario and Ragusa, Francesco and Farinella, Giovanni Maria and Furnari, Antonino},
  booktitle={International Conference on Image Analysis and Processing},
  pages={13--24},
  year={2023},
  organization={Springer}
}

Table of Contents

• Introduction
• Installation
• Data Preparation
• Evaluation

Introduction

This repository contains the code for the quasi-online action detection module of the paper "Quasi-Online Detection of Take and Release Actions from Egocentric Videos".

The ENIGMA-51 dataset has been released, you can find the paper here and the project page here. We have also a repository on Github, go check it out.

Our Online Action Prediction module is based on TeSTra, refer to the original repository for more information and instructions.

Installation

To set up the environment for this project, you can use either pip or Conda.

Using pip

If you prefer using pip, you can install the required packages by running the following command:

pip install -r requirements.txt

Using Conda

Alternatively, you can use Conda to create an environment with the necessary dependencies. First, create the environment using the requirements.txt file:

conda create --name <env_name> --file requirements.txt

If you encounter any issues, try adding the conda-forge channel to your list of channels:

conda config --append channels conda-forge

This will instruct Conda to search for packages on the conda-forge channel as well.

Creating an Environment from a .yml File

You can also create a Conda environment directly from the environment.yml file:

conda env create --name environment_name --file environment.yml

Replace <env_name> and environment_name with your desired environment names. This will set up all necessary dependencies as specified in the configuration files.

Data Preparation

We provide the checkpoint of TeSTra for the THUMOS dataset, which can be downloaded from here and placed inside the data/TeSTra/THUMOS/checkpoints/ folder. This step is not mandatory if you only want to test the quasi-online action detection module. Use the configuration file config/TeSTra/THUMOS/testra_long_512_work_8_kinetics_1x_box to run the TeSTra network on the THUMOS dataset.

We also provide ground truth action starts, predictions from the TeSTra network, and results from the model experiments for both the THUMOS and ENIGMA datasets. Download these files from here and place them in the data/ folder.

The structure of the data/ folder should look like this:

Quasi-Online-Detection-Take-Release 
│
├── data/
│   ├── TeSTra/
│       ├── THUMOS/
│           ├── checkpoints/
│               ├── testra_th14_long_512_work_8_box.epoch-15.pth
│               ├── testra_th14_long_512_work_8_box.epoch-15.txt
│   ├── gt_action_starts/
│       ├── enigma_gt_action_starts.pkl
│       ├── thumos_gt_action_starts.pkl
│   ├── predictions/
│       ├── enigma_predictions.pkl
│       ├── thumos_predictions.pkl
│   ├── model_experiments/
│       ├── ENIGMA/
│           ├── enigma_results.csv
│           ├── test_ws5_it0_s1_md10.pkl
│           ├── test_ws5_it0_s2_md10.pkl
│           ├── ...
│       ├── THUMOS/
│           ├── thumos_results.csv
│           ├── test_ws4_it0_s1_md8.pkl
│           ├── test_ws4_it0_s2_md8.pkl
│           ├── ...
│
├── ...

Evaluation

To evaluate the quasi-online action detection module, you can use the main.py script. The script supports different modes of operation: single_test, grid_search, and performance_over_latency. Each mode corresponds to a specific evaluation scenario.

To run a single test using the default configuration, use the following command:

python main.py --config_path config/enigma_config.yaml --mode single_test

This command will use the configuration file specified in config/enigma_config.yaml to perform a single test evaluation using the parameters defined in the configuration file.

The --mode argument determines the mode of operation. Depending on the mode specified, it will then execute the corresponding evaluation function:

• single_test: Evaluate the model using a single set of parameters defined in the configuration file.
• grid_search: Perform a grid search over different parameter sets defined in the src/run_tests.py file.
• performance_over_latency: Evaluate the model's performance against different latency thresholds, as defined in the src/run_tests.py file.

For more information on the configuration parameters, refer to the config/config_loader.py file. This file provides detailed documentation on how to set up your configuration file to match your specific needs. By following these instructions, you can easily evaluate the quasi-online action detection module with different settings and datasets.