Development of a Real-Time Video Surveillance System using Enhanced Fuzzy Based Serial Artificial Neural Networks for Transportation Applications
Keywords:
Real-time video surveillance system, transportation applications, enhanced fuzzy-based serial artificial neural network (EF-SANN), Z-score normalization, effective monitoringAbstract
To ensure public safety and security in transportation applications, video surveillance systems are essential. Intelligent surveillance systems with real-time analysis and decision-making capabilities are becoming more and more in demand as a result of the complexity of transportation networks and the necessity for effective monitoring. This study describes creating an advanced real-time video surveillance system for transportation applications that uses an enhanced fuzzy-based serial artificial neural network (EF-SANN). The dataset comprises real-world video footage shot in various transportation-related locations, such as motorways, bus terminals, and traffic crossroads. The video data includes a variety of scenarios, including traffic, people walking, and crowd behavior. Preprocessing using the Z-score normalization is employed to enhance the quality and usability of the dataset. These techniques encompass video stabilization, noise reduction, frame extraction, and object annotation. The preprocessed dataset is the basis for EF-SANN architecture development and evaluation. The system uses serial artificial neural networks and advanced fuzzy logic for object detection, tracking, behavior analysis, and anomaly detection. The studies performed with the dataset show how the EF-SANN approach is effective in accomplishing accurate real-time monitoring goals. The system demonstrates excellent object detection and tracking precision, successfully analyzes object behaviors, and successfully identifies anomalous actions.
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