Daniel Pascual
Papers
1
Total Citations
5
H-Index
1
About
Daniel Pascual is a researcher specializing in multisensor fusion, supervised learning, and particle filtering for intelligent perception systems. His most cited work, "A Multisensor Based Approach Using Supervised Learning and Particle Filtering for People Detection and Tracking" (2015), has garnered 5 citations, demonstrating a focused contribution to the field of autonomous systems and robotics. Pascual's research integrates advanced machine learning techniques with probabilistic filtering to enhance the accuracy and robustness of human detection and tracking in dynamic environments. This work is particularly impactful for applications in autonomous vehicles, surveillance, and human-robot interaction, where reliable perception is critical. By combining supervised learning with particle filtering, Pascual addresses challenges in sensor noise and occlusion, offering a scalable solution for real-time tracking. His contributions underscore a commitment to bridging theoretical algorithms with practical implementations, making his research valuable for students and engineers developing next-generation perception systems.
Research Focus
Key Achievements
Top Papers
- 1