Papers
1
Total Citations
7
H-Index
1
About
Jie Ou has made significant contributions to the field of computer vision, with a primary focus on pedestrian detection—a critical component for applications such as autonomous driving, intelligent surveillance, and robotics. Ou’s most notable work, "Feature Fusing of Feature Pyramid Network for Multi-Scale Pedestrian Detection" (2018), addresses a fundamental challenge in the field: detecting pedestrians of varying sizes within a single image. By innovatively fusing features across different scales of a feature pyramid network, Ou’s approach enhances detection accuracy for both small and large pedestrians, improving robustness in complex real-world scenes. This work has garnered 7 citations, reflecting its relevance to ongoing research in multi-scale object detection. Ou’s research is particularly impactful for safety-critical systems, where reliable pedestrian detection is paramount. Through this contribution, Ou has helped advance the practical deployment of vision-based systems, making strides toward safer autonomous navigation and more effective surveillance.
Research Focus
Key Achievements
Top Papers
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