19 May 2016 Multispectral image fusion for vehicle identification and threat analysis
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Abstract
Unauthorized vehicles become an increasing threat to US facilities and locations especially overseas. Vehicle detection is a well-studied area. However, vehicle identification and intension analysis have not been sufficiently investigated. We propose to use multispectral (visible, thermal) images (1) to match the vehicle types with the registered (or authorized) vehicle types; (2) to analyze the vehicle moving patterns, (3) and study methods to utilize open information such as GPS and traffic information. When a vehicle is either permitted to access to the facility, or subjected to further manual inspection (scrutiny), the additional information (e.g., text) can be compared against the imagery features. We use information fusion (at image, feature, and score level) and neural network to increase vehicle matching accuracy. For the vehicle moving patterns, we will classify them as “normal” and “abnormal” by using driving speed, acceleration, stop, zig-zag, etc. The methods would support directions in physical and human-based sensor fusion, patterns of life (POL) analysis, and contextual-enhanced information fusion.
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Yufeng Zheng, Erik Blasch, "Multispectral image fusion for vehicle identification and threat analysis", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710G (19 May 2016); doi: 10.1117/12.2228772; https://doi.org/10.1117/12.2228772
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