End-to-end human parsing and detection optimized for resource-constrained devices

Hosen M. I., Aydin T., Islam M. B.

Scientific Reports, vol.16, no.1, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 1
  • Publication Date: 2026
  • Doi Number: 10.1038/s41598-025-30449-9
  • Journal Name: Scientific Reports
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, MEDLINE, Directory of Open Access Journals
  • Keywords: Multi-human parsing, Polygons annotation, Resource-constrained devices, Self-attention
  • İstanbul Ticaret University Affiliated: Yes

Abstract

Human parsing, a vital task in human-centric analysis, involves segmenting clothing and body parts for individual association. Existing methods often rely on auxiliary inputs like detection and edge prediction, limiting their suitability for resource-constrained devices. To address this, we propose an end-to-end framework that integrates a transformer based self-attention module to enhance contextual understanding while being optimized for low-resource environments. We also introduce bounding-polygon annotations to facilitate simultaneous detection and parsing. Our method achieves fine-grained results in a single pass, significantly improving inference speed without sacrificing accuracy. Real-world validation on Raspberry Pi demonstrates its effectiveness and efficiency in resource-constrained scenarios.