Qiongpei Xia
Papers
1
Total Citations
46
H-Index
1
About
Qiongpei Xia has made significant contributions to computer vision, with a particular focus on pedestrian detection—a critical technology for autonomous driving, video surveillance, and robotics. Their most cited work, the 2020 review “Deep learning for occluded and multi‐scale pedestrian detection: A review” (46 citations), provides a comprehensive analysis of deep learning approaches to two of the field’s most persistent challenges: occlusion and scale variation. This review synthesizes advances in convolutional neural networks, attention mechanisms, and multi-scale feature fusion, offering a valuable roadmap for researchers tackling real-world detection scenarios. By systematically categorizing methods and identifying open problems, Xia’s work has helped guide subsequent innovations in robust pedestrian detection systems. Their research bridges theoretical progress with practical deployment needs, addressing how algorithms can maintain accuracy when pedestrians are partially hidden or appear at varying distances. With a citation record that underscores the relevance of their contributions, Qiongpei Xia continues to influence the development of safer, more reliable vision-based perception systems for intelligent transportation and surveillance applications.
Research Focus
Key Achievements
Top Papers
- 1Deep learning for occluded and multi‐scale pedestrian detection: A review46 citations · 2020