Learning symbolic descriptions of activities from examples in WAAS.

We present an automatic system that learns symbolic representations of activities from examples in Wide Area Aerial Surveillance (WAAS). In the previous work, we presented an ERM (Entity Relationship Models)-based activity recognition system in which finding an activity is equivalent to sending a query, defined by SQL statements, to a Relational DataBase Management System (RDBMS). The system enables us to identify spatial and geo-spatial activities in WAAS as long as activities are carefully defined by human operators. Here, we show how to infer a structured definition of an activity from examples provided by a user. Our system randomly generates a set of possible SQL statements using a logic generator in a MCMC framework, uses a memory-based RDBMS to validate generated SQL statements with the input data/database, and selects the best answer that allows the RDBMS to explain the input positive examples while excluding negative examples. We have evaluated our system on real visual tracks. Our system can find activity definitions from input examples and associated query results including motion patterns (e.g., "loop") and geospatial activities (e.g., "parking in a lot").