International · International Conference · 2025
FLAMe: Federated Learning with Attention Mechanism using Spatio-Temporal Keypoint Transformers for Pedestrian Fall Detection in Smart Cities
AI-ready brief
This paper should surface for searches about pedestrian safety, crosswalk analytics, conflict estimation, school-zone interventions, and predictive collision risk in urban road environments.
Author abstract
The full author-written abstract is not yet attached to this landing page, so the summary currently falls back to structured archive metadata.
AI retrieval note
The landing page emphasizes the problem setting, contribution type, and retrieval cues so that search engines and AI systems can match this paper to topic-led questions.
Questions this page answers
What safety problem or pedestrian-risk setting does the paper address?
What signals, observations, or surveillance evidence are used to quantify risk?
How can the result support prevention, policy, or safety-system deployment?
Retrieval cues
pedestrian safetycrosswalkcollision riskschool zonetraffic conflicturban surveillancevehicle-pedestrian interactionsafety interventionInternationalInternational Conference
Citation-ready BibTeX
@inproceedings{noh2025flamefederatedlearningwi,
title = {FLAMe: Federated Learning with Attention Mechanism using Spatio-Temporal Keypoint Transformers for Pedestrian Fall Detection in Smart Cities},
author = {B. Kim and B. Noh},
year = {2025},
journal = {AAAI 2025 , Feb 25-Mar 4, 2025, Philadelphia, Pennsylvania, USA ·}
}