Yongheng Zhao

Papers

Total Citations

H-Index

About

Yongheng Zhao is a computer vision researcher whose work focuses on robust multi-object tracking and RGB-D perception systems. His most cited paper, "Robust multiple object tracking in RGB-D camera networks" (2017, 8 citations), introduces an enhanced version of the MeanShift tracking algorithm that leverages depth information from RGB-D sensors. Zhao's key contribution lies in three significant improvements to the original MeanShift method: incorporating depth data to handle occlusions and scale changes, enabling seamless tracking across multiple camera viewpoints, and maintaining computational efficiency for real-time applications. This work addresses critical challenges in camera network surveillance, where objects must be consistently tracked as they move between different sensors' fields of view. While his citation count reflects the specialized nature of this emerging field, Zhao's research has practical implications for smart surveillance systems, human-computer interaction, and autonomous navigation. His approach to fusing color and depth information for robust tracking represents an important step forward in making multi-camera tracking systems more reliable in real-world environments with complex lighting conditions and occlusions.