Ran Zang
Papers
1
Total Citations
5
H-Index
1
About
Ran Zang is a researcher in robotics and computer vision, with a focus on real-time object tracking for autonomous systems. His work centers on developing efficient, monocular tracking schemes that enable service robots to lock onto and follow specified targets using deep learning architectures. In his notable 2018 paper, "Monocular Robot Tracking Scheme Based on Fully-Convolutional Siamese Networks," Zang designed a tracking system that leverages Siamese convolutional networks as the core tracker, allowing a mobile robot to robustly follow a target using only a single camera. This contribution addresses a fundamental challenge in service robotics—reliable, low-latency target tracking without expensive sensor suites. While his most-cited work has garnered 5 citations, it represents a focused step toward integrating deep learning with practical robotic control. Zang’s research bridges the gap between advanced neural network architectures and real-world robotic applications, offering a streamlined approach to visual tracking that prioritizes both accuracy and computational efficiency. His work is particularly relevant for students and researchers exploring lightweight vision systems for autonomous navigation and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1