[Research Group Page] [Paper Link]
This repository presents a new method based spatiotemporal features to perform Human Action Recognition from videos.
Ilustration of architeture of proposed method:
The advances in video capture, storage and sharing technologies have caused a high demand in techniques for automatic recognition of humans actions. Among the main applications, we can highlight surveillance in public places, detection of falls in the elderly, no-checkout-required stores (Amazon Go), self-driving car, inappropriate content posted on the Internet, etc. The automatic recognition of human actions in videos is a challenging task because in order to obtain a good result one has to work with spatial information (e.g., shapes found in a single frame) and temporal information (e.g., movements found across frames). In this work, we present a simple methodology for describing human actions in videos that use extracted data from 2-Dimensional poses. The experimental results show that the proposed technique can encode spatial and temporal information, obtaining competitive accuracy rates compared to state-of-the-art methods.
If you find this work helpful for your research, please cite our following paper:
M. Varges and A. N. Marana. Human Action Recognition using 2D Poses. 8th Brazilian Conference on Intelligent Systems (BRACIS), 2019.
@inproceedings{har2dposes_bracis2019,
title = {Human Action Recognition using 2D Poses},
author = {Murilo Varges da Silva and Aparecido Nilceu Marana.},
booktitle = {2019 8th Brazilian Conference on Intelligent Systems (BRACIS)},
year = {2019},
keywords={Human action recognition; Surveillance systems; Spatio-temporal features; Videos sequences},
}
If you have any question or feedback about the code, please contact: [email protected].
To run this project you will need to install the follows softwares:
- OpenPose Framework (Optional)
- Python version 3.6 or higher
This project requires the following dependencies:
matplotlib==2.2.2
numpy==1.15.0
pandas==0.22.0
seaborn==0.9.0
scikit_learn==0.21.3
Install the requirements with command:
pip install -r requirements.txt
We provide some basic tutorials for you to get familar with the code and tools.
This project is Apache 2.0 licensed, as found in the LICENSE file.
We thank NVIDIA Corporation for the donation of the GPU used in this study. This study was financed in part by CAPES - Brazil (Finance Code 001).