mlsanityData to 7 Oct 2026

Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems

arXiv 2108.10506 ↗First posted 24 Aug 2021cs.CV cs.ROcited by 4

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Abstract (arXiv)

Estimating a scene's depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field. This paper proposes a novel, low complexity network architecture for fast and accurate human depth estimation and segmentation in indoor environments, aiming to applications for resource-constrained platforms (including battery-powered aerial, micro-aerial, and ground vehicles) with a monocular camera being the primary perception module. Following the encoder-decoder structure, the proposed framework consists of two branches, one for depth prediction and another for semantic segmentation. Moreover, network structure optimization is employed to improve its forward inference speed. Exhaustive experiments on three self-generated datasets prove our pipeline's capability to execute in real-time, achieving higher frame rates than contemporary state-of-the-art frameworks (114.6 frames per second on an NVIDIA Jetson Nano GPU with TensorRT) while maintaining comparable accuracy.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 37 of its 50 references that are in the map. First posted on arXiv 24 Aug 2021.

Builds on

37 of 50 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1.

4 papers.

  1. 2022Deep Learning on Home Drone: Searching for the Optimal Architecturecited by 4
  2. 2022An Embedded Monocular Vision Approach for Ground-Aware Objects Detection and Position Estimationcited by 1
  3. 2025NeoARCADE: Robust Calibration for Distance Estimation to Support Assistive Drones for the Visually Impairedcited by 1
  4. 2024Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimationcited by 0