Probabilistic spatio-temporal retrieval in smart spaces
A smart space is one that automatically identifies and tracks its occupants using unobtrusive biometric modalities such as face, gait, and voice in an unconstrained fashion. Information retrieval in a smart space is concerned with the location and movement of people over time. Towards this end, we abstract a smart space by a probabilistic state transition system in which each state records the probabilities of presence of individuals in various zones of the smart space. We carry out track-based reasoning on the states in order to determine more accurately the occupants of the smart space. This leads to a data model based upon an occupancy relation in which time is treated discretely, owing to the discrete nature of events, but probability is treated as a real-valued attribute. Using this data model, we show how to formulate a number of spatio-temporal queries, focusing on the computation of probabilities, an aspect that is novel to this model. We present queries both in SQL syntax and also in CLP(R), a constraint logic programming language (with reals) which facilitates succinct formulation of recursive queries. We show that the answers to certain queries are better displayed in a graphical manner, especially the movement tracks of occupants of the smart space. We also define query-dependent precision and recall metrics in order to quantify how well the model is able to answer various spatio-temporal queries. We show that a query-dependent metric gives significantly better results for a class of occupancy-related queries compared with query-independent metrics.
retrieval smart spaces biometrics data model spatio-temporal queries clp(r) precision recall abstract framework recognition