Papers
8
Total Citations
50
H-Index
4
About
Yoichi Tomioka is a researcher whose work sits at the intersection of robotics, computer vision, and intelligent surveillance. His primary research areas include mobile robot patrol planning, video surveillance optimization, and depth-image-based navigation. Tomioka’s most significant contribution is in developing algorithms for generating optimum patrol courses for mobile surveillance cameras, enabling efficient observation of wide or complex areas with fewer resources. His 2011 paper on this topic has garnered 22 citations, reflecting its foundational impact. He has also advanced obstacle avoidance for indoor patrol robots using depth imaging, addressing the critical limitation of lighting dependency in traditional vision-based systems. Beyond surveillance, Tomioka has explored hardware-efficient AI, proposing a random-forest-based approximation layer for binary and ternary CNN accelerators, targeting low-latency robot control. His work on collaborative patrol planning for multiple cameras and robust self-localization in multi-robot environments further underscores his contributions to autonomous systems. With a career spanning over a decade, Tomioka continues to bridge theoretical algorithms and practical deployment in security and robotics.
Research Focus
Key Achievements
Top Papers
- 1Generation of an Optimum Patrol Course for Mobile Surveillance Camera22 citations · 2011
- 2Depth Image-Based Obstacle Avoidance for an In-Door Patrol Robot9 citations · 2019
- 3
- 4
- 5
- 6
- 7
- 8Travelling route of mobile surveillance camera2 citations · 2010