Xiankai Chen
Papers
1
Total Citations
26
H-Index
1
About
Xiankai Chen is a computer vision researcher whose work focuses on pedestrian detection and intelligent transportation systems. His most-cited paper, "Integrating Orientation Cue With EOH-OLBP-Based Multilevel Features for Human Detection" (2013, 26 citations), addresses a fundamental challenge in autonomous driving and robotics: detecting pedestrians efficiently and accurately from on-board cameras. Chen introduced a novel pedestrian detection system that combines orientation cues with enhanced features, specifically integrating Edge Orientation Histograms (EOH) and Overlapping Local Binary Patterns (OLBP) at multiple scales. This multilevel feature approach significantly improved detection accuracy in real-world scenarios, contributing to safer smart car systems. His research bridges the gap between theoretical feature engineering and practical deployment in autonomous vehicles. Chen's work has been influential in advancing monocular camera-based perception systems, providing a foundation for subsequent studies in pedestrian safety and intelligent transportation. His contributions demonstrate a keen understanding of both algorithmic innovation and real-world application requirements in computer vision.
Research Focus
Key Achievements
Top Papers
- 1