Analysis of Deep Learning 3-D Imaging Methods Based on UAV SAR

Liu, Changhao; Wang, Yan; Ding, Zegang; Wei, Yangkai; Huang, Jianhui; Cai, Yawen

doi:10.1109/igarss46834.2022.9883292

Cited by 2 publications

(1 citation statement)

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“…By applying deep learning techniques, the automatic estimation of skeletal poses can be achieved without hand-crafted features. However, training deep learning models is data greedy and requires a large amount of manually labeled data to guarantee performance [9][10][11]. On the scattering mechanism of an electromagnetic wave, optical scattering mainly occurs when visible and infrared wavelengths (350-1000 nm) perceived by visual sensors interact with targets.…”

Section: Introductionmentioning

confidence: 99%

Vulnerable Road User Skeletal Pose Estimation Using mmWave Radars

Zeng,

Liang,

et al. 2024

Remote Sensing

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A skeletal pose estimation method, named RVRU-Pose, is proposed to estimate the skeletal pose of vulnerable road users based on distributed non-coherent mmWave radar. In view of the limitation that existing methods for skeletal pose estimation are only applicable to small scenes, this paper proposes a strategy that combines radar intensity heatmaps and coordinate heatmaps as input to a deep learning network. In addition, we design a multi-resolution data augmentation and training method suitable for radar to achieve target pose estimation for remote and multi-target application scenarios. Experimental results show that RVRU-Pose can achieve better than 2 cm average localization accuracy for different subjects in different scenarios, which is superior in terms of accuracy and time compared to existing state-of-the-art methods for human skeletal pose estimation with radar. As an essential performance parameter of radar, the impact of angular resolution on the estimation accuracy of a skeletal pose is quantitatively analyzed and evaluated in this paper. Finally, RVRU-Pose has also been extended to the task of estimating the skeletal pose of a cyclist, reflecting the strong scalability of the proposed method.

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Section: Introductionmentioning

confidence: 99%