Gaussian Mixture Models and Split-Merge Algorithm for parameter analysis of tracked video objects
GuoQing Yin, Dietmar Bruckner · 2009
Parameters of tracked video objects (for example: the angles of moving objects) are discrete random variables and the amount of data increases over time. In this paper we use a new method to analyze the parameter angle: the video frame is segmented into small sections and in each section the angle values during some time period are gathered. Through analysis the angle data in each section these angles can be modeled, therefore also in whole frame. The build model will be used to find abnormal behavior of moving objects. To build a statistical model of the angle of moving objects from the video data is a question of cluster analysis in real time. For this application, Gaussian mixture models and split-merge algorithm provide a powerful solution.