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Leveraging The Data Gathering and Analysis Phases to Gain Situational Awareness



Situational Awareness (SA) is the process of collecting observations, understanding their meaning and projection of new possible updates. It is applied in several dynamic and complex environments like aviation, air traffic control, power plant operations, military command and control. Object classification in infrared images is a crucial component of smart surveillance systems. In this paper, a robust framework for person-vehicle classification is proposed, which classifies objects with 97.2% accuracy and successfully achieves its intended use in challenging real-world situations. Some of these challenges are: Random camera viewpoints and low resolution infrared images. The main concern of this framework is enhancing the preprocessing phase to achieve higher classification accuracy. This paper provides an improved classification process accompanied with interpolation process for extracting the concerned features (objects) in infrared images. This framework increases the classification accuracy and overcome some potential problems in the data analysis phase of Situational Awareness (SA).The proposed system is simulated using MATLAB environment and achieves accurate results by relying on powerful distinguishable features extraction, high productive preprocessing workflow, high quality interpolation and classification framework. Experimental results prove the effectiveness of the proposed framework. The obtained results are compared against the results obtained from other technique.



Total Pages: 20
Pages: 523-542


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Volume: 21
Issue: 4
Year: 2015

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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