Abstract
The cluster behavior has the characteristics of large spatiotemporal scale, multi-source heterogeneous information, and complex target features. Most of the existing cluster behavior understanding methods are based on the analysis of monitoring images, and their scene definition is narrow. The real scene not only contains the data information collected by the surveillance camera but also the knowledge information outside the scope of monitoring, such as the experience of police officers and relevant rules. Simultaneously considering the data and knowledge not only reduces the misjudgment but also reduces the processing task of the machine and improves the recognition speed. In this paper, data and knowledge are integrated to conduct scene modeling, which can describe the scene more comprehensively and accurately. Based on the scene information element constructed by multi-source heterogeneous information, the cluster behavior understanding system is built to improve the speed and accuracy of cluster behavior identification.
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