Jingyi Jin
Papers
1
Total Citations
25
H-Index
1
About
Jingyi Jin is making impactful strides in 3D computer vision for autonomous driving, with a focus on multi-object detection and tracking (MODT). Her most-cited work, “Boost Correlation Features with 3D-MiIoU-Based Camera-LiDAR Fusion for MODT in Autonomous Driving” (2023, 25 citations), addresses a critical challenge: leveraging multimodal sensor data—specifically from cameras and LiDAR—to improve tracking accuracy in dynamic environments. By introducing a novel 3D-MiIoU-based fusion method, Jin enhances correlation features between objects, enabling more robust and reliable detection and tracking in real-world driving scenarios. This work is pivotal for advancing autonomous vehicle perception systems, where precise object tracking is essential for safety and navigation. Jin’s research sits at the intersection of computer vision, sensor fusion, and robotics, contributing to the broader goal of fully autonomous mobility. With her innovative approach to multimodal integration, she is helping to bridge the gap between theoretical models and practical deployment in complex, real-time environments. Her work is a valuable resource for students and researchers exploring 3D object tracking and sensor fusion in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1