Adaptive Multi-Modal Deep Learning Framework for Proactive Crime Detection and Behavioral Analysis in Smart City Surveillance Networks

This research introduces an Adaptive Multi-Modal Deep Learning Framework for proactive crime detection and behavioral analysis, aimed at enhancing the effectiveness of smart city surveillance networks. Our framework integrates historical crime data analysis with real-time video monitoring to provide a comprehensive solution that detects criminal behaviors and predicts potential crimes before they occur. Testing in diverse urban environments shows a 92% improvement in detection accuracy and a significant reduction in response times.