Hauptseite > Publikationsdatenbank > A Novel Dataset for Detecting Pedestrian Heads in Crowds Using Deep Learning Algorithms > print

001     1034451
005     20241219210859.0
024 7 _ |a 10.34734/FZJ-2024-07217
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037 _ _ |a FZJ-2024-07217
041 _ _ |a English
100 1 _ |a Abubaker, Mohammed
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111 2 _ |a Traffic and Granular Flow
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|d 2024-12-02 - 2024-12-05
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245 _ _ |a A Novel Dataset for Detecting Pedestrian Heads in Crowds Using Deep Learning Algorithms
260 _ _ |c 2024
336 7 _ |a Conference Paper
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520 _ _ |a The automatic detection of pedestrian heads in crowded environments is crucial for various crowd analysis and management tasks, including crowd counting, density estimation, pedestrian trajectory extraction, and behavior detection. Despite advancements in deep learning algorithms for object detection, existing studies struggle with pedestrian head detection in crowded situations such as railway platforms and event entrances, where risks frequently arise. One main reason for the poor head detection performance is the underrepresentation of such scenarios in existing datasets. These scenarios are particularly challenging due to variations in lighting conditions, viewpoints, occlusions, scale changes, indoor/outdoor environments,and weather conditions.To narrow this gap, we introduce a novel, diverse, and high-resolution dataset of human heads in crowds at Railway Platforms and Event Entrances, named the RPEE-Heads dataset.
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700 1 _ |a Alsadder, Zubayda
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700 1 _ |a Abdelhaq, Hamed
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700 1 _ |a Chraibi, Mohcine
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700 1 _ |a Boltes, Maik
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700 1 _ |a Alia, Ahmed
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|b 5
|e Corresponding author
856 4 _ |u https://juser.fz-juelich.de/record/1034451/files/A%20Novel%20Dataset%20for%20Detecting%20Pedestrian%20Heads%20in%20Crowds.pdf
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