Title :
Gaussian models and fast learning algorithm for persistence analysis of tracked video objects
Author :
Yin, GuoQing ; Bruckner, Dietmar
Author_Institution :
Inst. of Comput. Technol., Vienna Univ. of Technol., Vienna
Abstract :
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.
Keywords :
Gaussian processes; embedded systems; image segmentation; learning (artificial intelligence); object detection; pattern clustering; tracking; video surveillance; Gaussian model; embedded system; machine learning algorithm; persistence object analysis; real time surveillance application; video frame segmentation cluster; video object tracking system; Algorithm design and analysis; Approximation algorithms; Entropy; Europe; Iterative algorithms; Layout; Machine learning algorithms; Predictive models; Real time systems; Surveillance; Gaussian Models; Machine Learning; Object Tracking; Parameter Analysis; Real-Time Applications;
Conference_Titel :
Human System Interactions, 2009. HSI '09. 2nd Conference on
Conference_Location :
Catania
Print_ISBN :
978-1-4244-3959-1
Electronic_ISBN :
978-1-4244-3960-7
DOI :
10.1109/HSI.2009.5090954
