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@MISC{Abubaker:1034460,
author = {Abubaker, Mohamad and Alsadder, Zubayda and Abdelhaq, Hamed
and Boltes, Maik and Alia, Ahmed},
title = {{RPEE}-{H}eads: {A} {B}enchmark for {P}edestrian {H}ead
{D}etection in {C}rowd {V}ideos},
address = {Jülich},
publisher = {Forschungszentrum Jülich},
reportid = {FZJ-2024-07226},
year = {2024},
abstract = {RPEE-Heads (Railway Platforms and Event Entrances-Heads) is
a new benchmark for pedestrian head detection in crowded
environments. It focuses on railway platforms and event
entrances, where risks frequently arise. The benchmark aims
to improve pedestrian head detection at railway platforms
and event entrances, helping to develop accurate deep
learning models for several crowd safety applications. It
includes: 1) A dataset comprising 109913 head annotations
across 1886 images, with an average of approximately 56.2
annotated heads per image. 2) An empirical comparative
analysis of eight state-of-the-art deep learning algorithms
for head detection was conducted across several publicly
available image datasets and the newly introduced RPEE-Heads
dataset. 3) An empirical study on head size’s impact on
detection algorithms’ performance.},
cin = {IAS-7},
cid = {I:(DE-Juel1)IAS-7-20180321},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511) / Pilotprojekt zur
Entwicklung eines palästinensisch-deutschen Forschungs- und
Promotionsprogramms 'Palestinian-German Science Bridge'
(01DH16027)},
pid = {G:(DE-HGF)POF4-5111 / G:(BMBF)01DH16027},
typ = {PUB:(DE-HGF)32},
doi = {10.34735/PED.2024.2},
url = {https://juser.fz-juelich.de/record/1034460},
}