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The authors present a machine learning technique that recognizes patterns of normal movement, using GPS data and time stamps, to gain the ability to detect regions of time containing abnormal movement. They argue people move throughout regions of time in established patterns, and a person's normal movement can be learned by machines. The authors use intelligent features extracted from raw GPS data with time stamps, to describe a person's movement over discrete regions of time. Then they use a nearest neighbor approach to determine outliers in a distribution of time regions.
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