Activity and function recognition for moving and static objects in urban environments from wide-area persistent surveillance inputs
2010
In this paper, we describe results from experimental analysis of a model designed to recognize activities and
functions of moving and static objects from low-resolution wide-area video inputs. Our model is based on
representing the activities and functions using three variables: (i) time; (ii) space; and (iii) structures. The
activity and function recognition is achieved by imposing lexical, syntactic, and semantic constraints on the
lower-level event sequences. In the reported research, we have evaluated the utility and sensitivity of several
algorithms derived from natural language processing and pattern recognition domains. We achieved high
recognition accuracy for a wide range of activity and function types in the experiments using Electro-Optical
(EO) imagery collected by Wide Area Airborne Surveillance (WAAS) platform.
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