PAVE and Urban Safety Edge Analytics

Real-time video analytics for pedestrian safety over edge and end devices.

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

  1. pave.webp
    Real-Time Video Analytics for Urban Safety: Deployment over Edge and End Devices
    Mahshid Ghasemi, Yongjie Fu, Xinyu Ouyang, Peiran Wang, Mehmet K 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, Jun 2025
    Best Paper Award
    Abs DOI HTML