FUSION OF IMAGE PROCESSING AND DATA MINING FOR OBJECT DETECTION AND TRACKING IN SURVEILLANCE SYSTEMS

This research provides the use of image processing and data mining methods in the detection of objects, object tracking, and detection of anomalies in a surveillance system. It analyses existing models and suggests a hybrid system based on qualitative methodology and secondary data analysis. Real-time speed, scalability, and flexibility would all be much improved, the data show. Particularly, data analysis and processing will have almost instantaneous latency, therefore enabling users to immediately gain insights for quick decision-making. Concerning scalability, the architecture of the system is meant to easily support exponential rise in user traffic and data volume without sacrificing performance, progressing from thousands of activities to millions of others. The increased flexibility also lets the platform change dynamically to meet changing company needs. This implies it can support bespoke setups, smoothly incorporate fresh data sources, and pivot operational processes on request. Finally, this strong mix changes the basic structure to allow for a more flexible, quick, and future-proof operating environment able of grasping developing opportunities and negotiating difficult hurdles with formerly unmatched effectiveness, which bring in a sturdier surveillance system.