Lucas A. Thomaz
Papers
1
Total Citations
20
H-Index
1
About
Lucas A. Thomaz is a researcher in computer vision and machine learning, with a focus on video surveillance and anomaly detection. His most cited work, "Anomaly detection with a moving camera using spatio-temporal codebooks" (2017, 20 citations), introduces a novel framework for identifying unusual events in dynamic environments where the camera itself is in motion—a challenging scenario often overlooked in static-camera approaches. By leveraging spatio-temporal codebooks, Thomaz’s method effectively models normal scene behavior and flags deviations, enabling robust surveillance in real-world settings like drone footage or vehicle-mounted cameras. This contribution addresses a critical gap in automated monitoring, offering practical solutions for security and situational awareness. With 20 citations, his work has influenced subsequent research in adaptive anomaly detection and motion-aware vision systems. Thomaz’s research underscores the importance of handling camera motion in video analytics, paving the way for more versatile and resilient surveillance technologies. His efforts continue to inspire students and researchers exploring the intersection of computer vision, machine learning, and real-world deployment challenges.
Research Focus
Key Achievements
Top Papers
- 1Anomaly detection with a moving camera using spatio-temporal codebooks20 citations · 2017