Towards real-world monitoring scenarios: An improved point prediction method for crowd counting based on contrastive learning.
In open environments, complex and variable backgrounds and dense multi-scale targets are two key challenges for crowd counting. Due to the reliance on supervised learning with labeled data, current methods struggle to adapt to crowd detection in complex scenarios when training data is limited; Moreo...
Saved in:
| Main Authors: | Rundong Cao, Jiazhong Yu, Ziwei Liu, Qinghua Liang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327397 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Crowd Counting Framework Combining with Crowd Location
by: Jin Zhang, et al.
Published: (2021-01-01) -
A point and density map hybrid network for crowd counting and localization based on unmanned aerial vehicles
by: Lei Zhao, et al.
Published: (2022-12-01) -
Crowd counting at the edge using weighted knowledge distillation
by: Muhammad Asif Khan, et al.
Published: (2025-04-01) -
ClassRoom-Crowd: A Comprehensive Dataset for Classroom Crowd Counting and Cross-Domain Baseline Analysis
by: Wenqian Jiang, et al.
Published: (2025-02-01) -
Label Noise Robust Crowd Counting with Loss Filtering Factor
by: Zhengmeng Xu, et al.
Published: (2024-12-01)