|
--- |
|
license: cc-by-4.0 |
|
task_categories: |
|
- audio-classification |
|
size_categories: |
|
- n>1T |
|
tags: |
|
- pedestrian detection |
|
--- |
|
# ASPED: An Audio Dataset for Detecting Pedestrians |
|
|
|
This repo contains the data for the ASPED dataset, presented at ICASSP 2024. |
|
- [Paper Link](https://arxiv.org/abs/2309.06531), [Project Homepage](https://urbanaudiosensing.github.io/ASPED.html) |
|
|
|
- Pavan Seshadri, Chaeyeon Han, Bon-Woo Koo, Noah Posner, Suhbrajit Guhathakurta, Alexander Lerch |
|
|
|
## Usage |
|
This dataset contains audio and video recordings of pedestrian activity collected at various locations in and around Georgia Tech. |
|
|
|
Labels of pedestrian counts per each second of audio/video are provided as well, calculated via a computer vision model (Mask2Former trained on msft-coco) using the video recordings. |
|
### Access |
|
It is recommended to use the huggingface_hub library to download the dataset from this location. [Info on downloading with huggingface_hub](https://huggingface.co/docs/huggingface_hub/guides/download). |
|
|
|
Downloading the entire dataset can be done with the following code: |
|
``` |
|
from huggingface_hub import snapshot_download |
|
snapshot_download(repo_id="pseshadri9/ASPED", repo_type="dataset") |
|
``` |
|
Alternatively if you would like to download only the audio or video, pass the ignore_patterns flag to snapshot_download to avoid downloading the entire set. |
|
|
|
**Audio Only** |
|
``` |
|
from huggingface_hub import snapshot_download |
|
snapshot_download(repo_id="pseshadri9/ASPED", repo_type="dataset", ignore_patterns="*.mp4") |
|
``` |
|
|
|
**Video Only** |
|
|
|
``` |
|
from huggingface_hub import snapshot_download |
|
snapshot_download(repo_id="pseshadri9/ASPED", repo_type="dataset", ignore_patterns="*.flac") |
|
``` |
|
|
|
|
|
## Citation |
|
``` |
|
@inproceedings{Seshadri24, |
|
title={ASPED: An Audio Dataset for Detecting Pedestrians}, |
|
author={Seshadri, Pavan and Han, Chaeyeon and Koo, Bon-Woo and Posner, Noah and Guhathakurta, Suhbrajit and Lerch, Alexander}, |
|
booktitle={Proc. of ICASSP 2024}, |
|
pages={1--5}, |
|
year={2024}, |
|
organization={IEEE} |
|
} |
|
``` |