Papers
7
Total Citations
88
H-Index
5
About
Jacinto C. Nascimento is a computer vision and robotics researcher whose work sits at the intersection of visual perception, deep learning, and autonomous systems. His research has made notable contributions to pedestrian detection, multi-sensor object tracking, and human-aware robot navigation — areas critical to the safe deployment of autonomous and collaborative robots in real-world environments. Nascimento's most influential work, "Tracking objects with generic calibrated sensors" (2010, 28 citations), introduced a robust algorithm leveraging color and 3D shape features for reliable object tracking. His sustained focus on pedestrian detection has yielded a compelling body of research, including pioneering real-time deep learning detectors that combine Aggregate Channel Features with convolutional neural networks, advancing both speed and accuracy in human detection for robotic navigation systems. His 2022 work on head pose estimation under occlusion conditions reflects a broadening toward nuanced human behavioral understanding, while his exploration of deep reinforcement learning on omnidirectional cameras demonstrates a commitment to pushing detection robustness across challenging imaging configurations. With publications spanning over a decade and citations accumulating across robotics, computer vision, and human-robot interaction communities, Nascimento represents a steady, technically rigorous voice advancing intelligent perception systems that enable machines to navigate and interact safely alongside people.
Research Focus
Key Achievements
Top Papers
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- 53D Tracking by Catadioptric Vision Based on Particle Filters9 citations · 2008
- 6A real-time Deep Learning pedestrian detector for robot navigation5 citations · 2017
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