Sparse Polytope Index for Image Retrieval in Surveillance

The patent transforms the traditional tree-based single-dimension grouping into a multi-dimensional feature space approach. By representing images as points in a high-dimensional space defined by multiple features (color, texture, shape, etc.), the system can differentiate objects that appear similar in traditional grouping methods. This dimensional expansion allows for more nuanced discrimination while maintaining efficient retrieval through geometric operations like polytope vertex identification.

The patent creates a compressed graphical representation (a copy) of the multi-camera surveillance data in the form of a polytope in high-dimensional space. Instead of requiring operators to manually aggregate information from numerous camera feeds, the system generates a mathematical model that captures the essential features and relationships across all cameras. This graphical copy can be efficiently queried and analyzed, making tracking and information aggregation tractable even with large numbers of cameras.

The patent performs preliminary organization of surveillance data by pre-computing the multi-dimensional feature representations and constructing the polytope structure from all available camera data before forensic analysis is needed. This preliminary action creates a ready-to-query graphical model where suspicious activities can be rapidly identified through targeted searches rather than manual sifting. The system pre-processes the data into a form that enables fast querying, so when forensic analysis is required, the comprehensive search can be conducted much more efficiently.