PAVE and Urban Safety Edge Analytics

PAVE-style urban safety analytics combine street camera perception, edge computing, and end device alerts to support real-time pedestrian safety while preserving privacy.

PAVE edge analytics and pedestrian alert workflow

The SEC 2025 paper received a Best Paper Award and demonstrates a scalable architecture for processing live video feeds, detecting pedestrians and vehicles, predicting vehicle trajectories, and sending anonymized danger-zone information to end-user devices.

Role: Co-author and applied AI contributor
Recognition: Best Paper Award, ACM/IEEE Symposium on Edge Computing 2025
Keywords: Edge video analytics, trajectory prediction, privacy-preserving warning systems, real-time deployment, pedestrian safety, city-scale sensing

Links: ACM DOI, Best Paper note, CS3 Situational Awareness.

References

2025

Real-Time Video Analytics for Urban Safety: Deployment over Edge and End Devices

Mahshid Ghasemi, Yongjie Fu, Xinyu Ouyang, Peiran Wang, Mehmet Kerem Turkcan, Jhonatan Tavori, Sofia Kleisarchaki, Thomas Calmant, Levent Gurgen, Zoran Kostic, Xuan Di, Gil Zussman, and Javad Ghaderi

In Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing, 2025

Best Paper Award

DOI HTML

PAVE is a privacy-preserving real-time video analytics system that uses edge and end-device processing to support pedestrian safety at urban intersections.