This paper proposed a novel solution to track human face obscured largely in an image on the basis of Mean Shift Tracing Algorithm (MSTA). The improved approach aims to update the target model in real-time during the whole tracking process to avoid target losing. Local Binary Pattern (LBP) theory is chosen to improve the original MSTA here. The experimental result shows that our new solution has a better performance in target tracking under situations like face rotation and occlusion as well as in fast acquisition when faces reappear on the screen.
The traffic administrative department would record real-time information of accidents and update the corresponding database when dealing with daily traffic routines. It is of great significance to study and analyze these data. In this paper, we propose a Multi-dimensional Data Warehouse Model (M-DWM) combined with the concept of Data Warehouse and multi-dimensional data processing theory. The model can greatly improve the efficiency for statistical analysis and data mining.
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