Dynamic Data Stream Management: Real-Time Unique Frameset Generation in Unsupervised Residential Surveillance

Abstract

Surveillance systems often struggle with managing dynamic spatial-temporal data streams due to information's vast volume and velocity. To address this, we introduce a novel self-acting framework tailored explicitly for generating unique framesets in an unsupervised video environment. Our approach leverages efficient data management techniques to extract and compress video data without losing critical details while filtering out irrelevant duplicated data, thereby enhancing real-time monitoring and analysis capabilities. We validate our methodology using a substantial dataset of one terabyte of surveillance footage, from which 26,454 unique frames were extracted, covering various activities and interactions within home premises, from daily chores to social interactions. The results demonstrate significant improvements in processing speed and data reduction, enabling better resource management. This research contributes to more effective home security solutions by providing a streamlined approach to managing dynamic data streams and identifying patterns often overlooked by traditional surveillance methods.