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Intelligent video monitoring system has been used widely in daily life. In order to avoid the casualties, as well as to predict potentially dangerous situations, real-time monitoring of crowd activities indoor has become an urgent requirement. In recent years, the video-based passenger flow counting systems have been improved a lot with constant equipment update. However, these systems are mostly aimed at a particular scene. In crowded circumstances, the statistical precision is not very satisfactory. The analysis of abnormal situations is thus imperfect. This paper improves the main algorithms, such as: the extraction of human, the segmentation of crowd and the judgment of human moving direction. Finally, the improved algorithms integrate a system which achieves two-way passenger flow counting. Consequently it first makes the early warning of abnormal conditions possible. The system's statistical accuracy is remarkably improved, and testing result under different scenes is shown.

Information Mining on Indoor Surveillance Video