TALOS Extended Abstract: Real-Time Edge-Deployed YOLOv8 for Bicycle Blind-Spot Monitoring

Summary

Unlock the extended abstract detailing TALOS's approach to real-time, edge-deployed computer vision for bicycle safety. Learn how YOLOv8-powered blind-spot monitoring on Raspberry Pi 5 enables scalable, low-latency safety tooling for urban mobility research. With practical design considerations, performance insights, and potential applications, this resource accelerates your work beyond standalone experiments.

Primary Outcome

Gained insights into TALOS's edge-deployed CV approach for real-time bicycle blind-spot monitoring, including performance metrics and practical design considerations.

Who This Is For

• - Graduate researchers in computer vision exploring edge deployment for transport safety
• - Engineers building affordable bike-safety demonstrations using Raspberry Pi
• - Professors and labs presenting urban mobility innovations seeking publishable case studies

What You'll Learn

• Edge deployment on Raspberry Pi 5
• YOLOv8-powered blind-spot monitoring
• Real-time bicycle safety application
• Urban mobility impact and scalability

Metadata

Category

AI

Creator

Kevin Davidson

Creator Title

Mathematics of Computation Student @ SMC | Data Structures SI Leader (Former) | Mapping my Campus @ Apollo

Tags

AI Tools, Automation, Productivity

Published

2026-03-15

Last Updated

2026-03-15

Citation

"TALOS Extended Abstract: Real-Time Edge-Deployed YOLOv8 for Bicycle Blind-Spot Monitoring" by Kevin Davidson, PlaybookHub — https://playbooks.rohansingh.io/playbook/talos-extended-abstract-real-time-edge-yolov8-bike-blind-spot

Canonical URL