Qiquan Zhang
Papers
1
Total Citations
3
H-Index
1
About
Qiquan Zhang is a researcher at the forefront of multi-modal perception for human-robot interaction, with a primary focus on speaker tracking and audio-visual sensor fusion. His most cited work, "GLMB 3D Speaker Tracking with Video-Assisted Multi-Channel Audio Optimization Functions" (2024, 3 citations), introduces a novel framework that integrates generalized labeled multi-Bernoulli filtering with video-assisted audio optimization to achieve robust 3D speaker localization. This contribution addresses a critical challenge in real-world HRI: the degradation of audio-only tracking in noisy or occluded environments. By leveraging complementary visual cues, Zhang’s method enhances tracking accuracy and reliability, paving the way for more natural and responsive robotic systems. His research has implications for assistive robotics, smart environments, and autonomous systems. While early in his career, Zhang’s work demonstrates a strong commitment to solving practical, sensor-driven problems, and his innovative use of multi-channel audio optimization functions marks a significant step forward in the field. As his citation count grows, his contributions are poised to influence both academic research and applied robotics.
Research Focus
Key Achievements
Top Papers
- 1