aSamantha D.F. Hilado
Papers
1
Total Citations
2
H-Index
1
About
Samantha D.F. Hilado’s research centers on computer vision and intelligent transportation systems, with a particular focus on pedestrian detection for safety-critical applications. Her most cited work, a 2012 study on vision-based pedestrian detection, demonstrates her expertise in combining Histogram of Oriented Gradients (HOG) feature descriptors with AdaBoost and Linear Support Vector Machines to create robust detection systems. This research directly addresses the growing need for reliable pedestrian recognition in advanced driver assistance systems and autonomous robotics—fields where accuracy can mean the difference between safety and harm. While her citation count of 2 reflects a focused, early-career contribution, the technical approach she employed remains foundational in object detection literature. Hilado’s work exemplifies the practical engineering mindset required to bridge computer vision algorithms with real-world deployment challenges. Her study contributes to the broader effort of making vehicles and robots more aware of their surroundings, a critical step toward safer autonomous systems. For students and researchers entering computer vision, Hilado’s work offers a clear, applied example of how classical machine learning techniques can solve pressing transportation safety problems.
Research Focus
Key Achievements
Top Papers
- 1