Information Mining on Indoor Surveillance Video
Yu Ming, Liu We · International journal of intelligent engineering and systems · 2010
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.