Last updated: 2026-03-15
By Kevin Davidson — Mathematics of Computation Student @ SMC | Data Structures SI Leader (Former) | Mapping my Campus @ Apollo
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.
Published: 2026-03-15
Gained insights into TALOS's edge-deployed CV approach for real-time bicycle blind-spot monitoring, including performance metrics and practical design considerations.
Kevin Davidson — Mathematics of Computation Student @ SMC | Data Structures SI Leader (Former) | Mapping my Campus @ Apollo
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.
Created by Kevin Davidson, Mathematics of Computation Student @ SMC | Data Structures SI Leader (Former) | Mapping my Campus @ Apollo.
- 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
Basic understanding of AI/ML concepts. Access to AI tools. No coding skills required.
Edge deployment on Raspberry Pi 5. YOLOv8-powered blind-spot monitoring. Real-time bicycle safety application. Urban mobility impact and scalability
$0.20.
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