Spatial-frequency data acquisition using rotational invariant pattern matching in smart environments
This article details the development and testing of an empirical data capture system with the ability to collect spatial-frequency statistics relating to the movement behaviour of a smart home inhabitant. This is achieved using a greyscale normalised cross-correlation pattern matching algorithm. Environmental obstructions on the floor space can also be inferred from a visual representation of the accumulated data. Whilst this methodology itself is not novel, its application to person tracking specifically within a smart home environment does not appear in the literature and is considered a novel approach. The results of tests performed on the pattern matching technique show a tracking competency rate of 94.45% with a standard deviation of 0.009027, indicating high fidelity across a wide variety of environmental factors.
pattern matching smart environments human positioning indoor tracking