Work

ProcNet: Deep Predictive Coding Model for Robust-to-occlusion Visual Segmentation and Pose Estimation

Year: 2023

Type: preprint

Abstract: Systems involving human-robot collaboration necessarily require that steps be taken to ensure safety of the participating human. This is usually achievable if accurate, reliable estimates of the human's pose are available. In this paper, we present a deep Predictive Coding (PC) model supporting visual segmentation, which we extend to pursue pose estimation. The model is designed to offer robustness to the type of transient occlusion naturally occurring when human and robot are operating in close proximity to one another. Impact on performance of relevant model parameters is assessed, and comparison to an alternate pose estimation model (NVIDIA's PoseCNN) illustrates efficacy of the proposed approach.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2310.18009

Authors: Zechmair, Michael, Bornet, Alban, Morel, Yannick

Institutions: Maastricht University, École Polytechnique Fédérale de Lausanne, University of Geneva

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Horizon 2020 Framework Programme

Awards: G434337789, Human Brain Project Specific Grant Agreement 2, G6607577419

OpenAlex ID: W4388032384

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