Matthias Gouveia
Papers
1
Total Citations
9
H-Index
1
About
Matthias Gouveia is a leading researcher in embedded computer vision and autonomous drone systems, with a focus on real-time object detection and tracking. His most cited work, "Real Time Pedestrian and Object Detection and Tracking-based Deep Learning: Application to Drone Visual Tracking" (2019), bridges classical image processing—such as improved Histogram of Oriented Gradients (HOG)—with modern deep learning approaches to enable robust, low-latency visual control for unmanned aerial vehicles. By optimizing detection algorithms for embedded platforms, Gouveia has advanced the practical deployment of AI-driven perception in constrained environments. His contributions are particularly impactful in autonomous navigation, surveillance, and human-robot interaction, where reliable real-time tracking is critical. With 9 citations on this seminal paper alone, his work continues to influence researchers developing lightweight, efficient vision systems for drones. Gouveia’s research exemplifies the synergy between traditional computer vision techniques and deep learning, offering scalable solutions for dynamic, real-world applications. His achievements underscore a commitment to making intelligent visual tracking accessible and effective for autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1