Yingdong Ma
Papers
1
Total Citations
26
H-Index
1
About
Yingdong Ma is a computer vision researcher whose work focuses on pedestrian detection and human-object interaction in intelligent systems. His most cited paper, "Integrating Orientation Cue With EOH-OLBP-Based Multilevel Features for Human Detection" (2013, 26 citations), addresses a critical challenge in autonomous driving and robotics: detecting pedestrians efficiently and accurately using on-board monocular cameras. In this work, Ma introduced a novel pedestrian detection system that combines orientation cues with enhanced multilevel features, specifically integrating Edge Orientation Histograms (EOH) with Overlapping Local Binary Patterns (OLBP). This approach significantly improved detection accuracy in complex urban environments, contributing to safer smart car systems. Ma's research sits at the intersection of computer vision, machine learning, and real-time safety systems, with implications for autonomous vehicles and assistive robotics. While his citation count reflects the specialized nature of his work, his contributions to feature extraction and multilevel representation for human detection have provided foundational techniques for subsequent researchers in pedestrian safety and intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
- 1