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Hauptverfasser: Seshadri, Pavan, Han, Chaeyeon, Koo, Bon-Woo, Posner, Noah, Guhathakurta, Subhrajit, Lerch, Alexander
Format: Preprint
Veröffentlicht: 2023
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2309.06531
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author Seshadri, Pavan
Han, Chaeyeon
Koo, Bon-Woo
Posner, Noah
Guhathakurta, Subhrajit
Lerch, Alexander
author_facet Seshadri, Pavan
Han, Chaeyeon
Koo, Bon-Woo
Posner, Noah
Guhathakurta, Subhrajit
Lerch, Alexander
contents We introduce the new audio analysis task of pedestrian detection and present a new large-scale dataset for this task. While the preliminary results prove the viability of using audio approaches for pedestrian detection, they also show that this challenging task cannot be easily solved with standard approaches.
format Preprint
id arxiv_https___arxiv_org_abs_2309_06531
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle ASPED: An Audio Dataset for Detecting Pedestrians
Seshadri, Pavan
Han, Chaeyeon
Koo, Bon-Woo
Posner, Noah
Guhathakurta, Subhrajit
Lerch, Alexander
Audio and Speech Processing
Sound
We introduce the new audio analysis task of pedestrian detection and present a new large-scale dataset for this task. While the preliminary results prove the viability of using audio approaches for pedestrian detection, they also show that this challenging task cannot be easily solved with standard approaches.
title ASPED: An Audio Dataset for Detecting Pedestrians
topic Audio and Speech Processing
Sound
url https://arxiv.org/abs/2309.06531