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Pose-aware monocular localization of occluded pedestrians in 3D scene space

Localization of pedestrians in 3D scene space from single RGB images is critical for various downstream applications. Current monocular approaches employ either the bounding box of pedestrians or the visible parts of their bodies for localization. Both approaches introduce additional error to the location estimation in the case of real-world scenarios – crowded environments with multiple occluded pedestrians. To overcome the limitation, this paper proposes a novel human pose-aware pedestrian localization framework to model poses of occluded pedestrians, where this enables accurate localization in image and ground space. This is done by proposing a light-weight neural network architecture, where this ensures a fast and accurate prediction of missing body parts for downstream applications. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of the framework compared to state-of-the-art in predicting pedestrians missing body parts as well as pedestrian localization.

Ketersediaan
08621.3678Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
621.3678
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
9 hlm PDF, 1.472 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
621.3678
Tipe Isi
text
Tipe Media
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Tipe Pembawa
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Edisi
Vol.2, December 2021
Subjek
Pedestrian localization
Human pose estimation
Occluded pedestrians modelling
Info Detail Spesifik
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Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Pose-aware monocular localization of occluded pedestrians in 3D scene space
    Localization of pedestrians in 3D scene space from single RGB images is critical for various downstream applications. Current monocular approaches employ either the bounding box of pedestrians or the visible parts of their bodies for localization. Both approaches introduce additional error to the location estimation in the case of real-world scenarios – crowded environments with multiple occluded pedestrians. To overcome the limitation, this paper proposes a novel human pose-aware pedestrian localization framework to model poses of occluded pedestrians, where this enables accurate localization in image and ground space. This is done by proposing a light-weight neural network architecture, where this ensures a fast and accurate prediction of missing body parts for downstream applications. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of the framework compared to state-of-the-art in predicting pedestrians missing body parts as well as pedestrian localization.
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