A smart vista-lite system for anomaly detection and motion prediction for video surveillance in vibrant urban settings

The Vista-Lite system labels major challenges in video surveillance involving computational complexity, restricted transferability over datasets, and the absence of an impactful approach to examine data from various cameras. This system emphasis three methodologies to solve these issues UOAL, TempoNet and BDSO. UOAL identifies abnormalities in video content via a segmentation approach improving accuracy in complex environments. TempoNet concentrates on forecasting motions and behaviors utilizing modern neural network frameworks, enhancing response times in identifying possibly malicious situations. BDSO enhances the computational resources by tuning system parameters thus assuring flexibility and decreasing false alarms. This fusion improves system persistence, sensibility and functional cost-efficiency making the solution versatile to vast surveillance scenarios. Comprehensive experiments using pedestrian, UCSD, and mall datasets established increased performance with 99% accuracy indicating the system’s capacity to maintain real-time, multi-camera data. Vista-Lite provides a novel, innovative, flexible approach to video surveillance combining anomaly detection, motion prediction, and resource optimization for improving and enhancing the domain.