Xingchen Zhang

Papers

Total Citations

H-Index

About

Xingchen Zhang’s research lies at the intersection of computer vision, autonomous systems, and privacy-preserving AI, with a primary focus on pedestrian behavior analysis. His most impactful work introduces a **dual-branch spatio-temporal graph neural network** for pedestrian trajectory prediction (2023, 56 citations), a breakthrough that addresses the critical challenge of modeling complex social interactions in dynamic environments. This innovation has direct applications in autonomous driving, robot path planning, and surveillance, enabling safer and more intelligent navigation systems. More recently, Zhang has tackled the pressing issue of privacy in intelligent transportation systems. His 2024 paper on **3PFS (Protecting Pedestrian Privacy Through Face Swapping)** proposes a novel method to anonymize pedestrians captured by vehicle-mounted cameras without compromising the data utility needed for deep learning model training. This work demonstrates his commitment to ethical AI deployment in real-world scenarios. With a growing citation footprint and a dual focus on advancing predictive accuracy while safeguarding individual privacy, Zhang is emerging as a thoughtful voice in responsible AI development, particularly for applications where human safety and rights intersect with technological progress.