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Recent years have seen significant progress in improving both the efficiency and effectiveness of time series classification. However, because the best solution is typically the Nearest Neighbor algorithm with the relatively expensive Dynamic Time Warping as the distance measure, successful deployments on resource constrained devices remain elusive. Moreover, the recent explosion of interest in wearable...
Extensive research on time series classification in the last decade has produced fast and accurate algorithms for the single-dimensional case. However, the increasing prevalence of inexpensive sensors has reinforced the need for algorithms to handle multi-dimensional time series. For example, modern smartphones have at least a dozen sensors capable of producing streaming time series, and hospital-based...
Based on the fact that most of the algorithms assume that the camera is fixed and the changing background is learned in the training period, a robust algorithm is proposed for complex background where a shaking camera, changing background and shadows are presented. It combines a new improved mixture of Gaussians model and a square neighborhood matching algorithm to eliminate shadows and reduce false...
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