In order to solve the object tracking under occlusion, the adaptively tracking algorithm is proposed based on color features. The object is adaptively divided using fuzzy k-means clustering algorithm, and the sub-regions are weighted with monotone decreasing kernel function. The object model is updated through mean value of sub-regions' colors, so the calculation is simple. During the object tracking, the method of integral matching is used; combining with the adaptive Kalman filter, the object tracking under occlusion is resolved effectively. The experiments show that the new algorithm can track the object exactly.
Synthetic Decision Support System is combined with online analytical processing, data mining, model libraries, databases and knowledge bases. It will become one of the key technologies of data management services of Internet of Things, and it will establish the foundation for the smart city. This paper researches on the sequential pattern mining for decision support, implements the PrefixSpan algorithm, and proposes some improved methods. The algorithm avoids the generation of candidate sets, thus improves the efficiency of the algorithm, but it is suitable mostly for short sequences mining areas.
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