Activity and function recognition for moving and static objects in urban environments from wide-area persistent surveillance inputs

Georgiy Levchuk; Aaron Bobick; Eric Jones

doi:10.1117/12.849492

15 April 2010 Activity and function recognition for moving and static objects in urban environments from wide-area persistent surveillance inputs

Georgiy Levchuk, Aaron Bobick, Eric Jones

Author Affiliations +

Proceedings Volume 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV; 77040P (2010) https://doi.org/10.1117/12.849492
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Georgiy Levchuk, Aaron Bobick, and Eric Jones "Activity and function recognition for moving and static objects in urban environments from wide-area persistent surveillance inputs", Proc. SPIE 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV, 77040P (15 April 2010); https://doi.org/10.1117/12.849492

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