Gaussian models and fast learning algorithm for persistence analysis of tracked video objects

Persistence of objects in scenes is an important parameter of video object tracking systems. From the analysis of objects' durations (of stay) we not only get how long they stay in the scene, but also precisely where the objects spend time. The video frame is therefore segmented into clusters, and objects which go through or stay there are assigned to that cluster. If we observe all objects in a time period we should get a model of object behavior with respect to duration for each cluster. Using the built model we try to find abnormal object behavior. To build a model of object's spatial duration from the video data we utilize Gaussians and fast learning algorithm for real time surveillance applications on embedded systems.