This research presents an innovative integrated approach for object detection, recognition, and classification in video surveillance systems, utilizing artificial intelligence techniques. Video surveillance is an essential tool for ensuring public safety and security in various environments, but the sheer volume of data generated makes manual analysis impractical.
The proposed approach combines state-of-the-art object detection algorithms, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), with powerful deep learning models for recognition and classification tasks. The system can automatically detect various objects, recognize their identities, and classify them into predefined categories.
By leveraging AI techniques, the system achieves high accuracy and efficiency, enabling real-time or near-real-time monitoring and analysis. Moreover, it can adapt to dynamic environments, handling complex scenarios and reducing false positives.
The integrated approach opens avenues for enhanced surveillance, anomaly detection, and potential automation of security-related processes. Its application can significantly improve public safety and security measures in critical areas such as airports, public spaces, and transportation hubs, supporting law enforcement and decision-making in various domains.