What type of study is this?

This is a Experimental Study study.

October 17, 2025Open Access

Multimodal Anomaly Detection for Urban Safety: A Real-World Implementation in Large-Scale Surveillance Systems

Key Points

The system achieved a detection precision of 93.4% while processing data, ensuring reliable urban safety.
Field trials across 28 zones showed the feasibility of real-time detection and highlighted the technology's effectiveness.
A CNN-LSTM architecture was utilized to integrate diverse data types, enhancing the accuracy of anomaly classification.
The study confirms the robustness of neural models for managing large-scale, cross-modal safety tasks in urban environments.

Abstract

Abstract Effectively identifying abnormal events in urban environments requires the fusion of diverse data modalities, including video, audio, and environmental sensors. This paper presents a neural network-based framework for real-time multimodal anomaly detection deployed in a live metropolitan scenario. Using a combined CNN-LSTM structure and attention-based feature alignment, the system integrates heterogeneous inputs to classify anomalies across 57 event types. Field deployment across 28 surveillance zones demonstrated a detection precision of 93.4% and average processing latency of 0.89 seconds per frame group. The results validate the feasibility and robustness of deploying neural models for large-scale, cross-modal urban safety tasks.

Read Full Paperexternally

AI से पूछें

Bookmark

View Full Paper