A Deep Neural Network Tool for Automatic Segmentation of Human Body Parts in Natural Scenes

2020/09/08 by Patrick McClure, Gabrielle E. Reimann, McClure, Patrick +5
Computer Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Human Pose and Action Recognition #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2009.09900

openalex publication_date 2020/09/08 · openalex created_date 2020/09/25 · openalex updated_date 2026/07/28

Abstract

This short article describes a deep neural network trained to perform automatic segmentation of human body parts in natural scenes. More specifically, we trained a Bayesian SegNet with concrete dropout on the Pascal-Parts dataset to predict whether each pixel in a given frame was part of a person's hair, head, ear, eyebrows, legs, arms, mouth, neck, nose, or torso.

Citations

• The Pascal Visual Object Classes (VOC) Challenge
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

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