Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 16, issue 2
Articles | Volume 16, issue 2
https://doi.org/10.5194/ms-16-877-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-16-877-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Research article
 | 
21 Nov 2025
Research article |  | 21 Nov 2025

A lightweight optimization framework for real-time pedestrian detection in dense and occluded scenes

Cui Chen, Jun Li, Zequn Shuai, Yiyun Wang, and Yaohong Wang

Viewed

Total article views: 908 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
719 165 24 908 22 31
  • HTML: 719
  • PDF: 165
  • XML: 24
  • Total: 908
  • BibTeX: 22
  • EndNote: 31
Views and downloads (calculated since 21 Nov 2025)
Cumulative views and downloads (calculated since 21 Nov 2025)

Viewed (geographical distribution)

Total article views: 852 (including HTML, PDF, and XML) Thereof 852 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Mar 2026
Download
Short summary
We developed a fast and compact model to detect pedestrians in crowded scenes, especially when people are partly hidden or far away. Our method improves how the model learns from small and difficult cases, and how it balances speed and accuracy. It runs much faster than current systems while maintaining similar accuracy, making it suitable for real-time use on mobile and edge devices.
Read more
Share