Title :
Fast learning algorithm for Gaussian models to analyze video objects with parameter size
Author :
Yin, GuoQing ; Bruckner, Dietmar
Author_Institution :
Inst. of Comput. Technol., Vienna Univ. of Technol., Vienna, Austria
Abstract :
Size of objects in scenes is an important parameter of video surveillance systems. From the analysis of object´s size we can build an objects size model in scenes. The basic idea derives from automatic calibration to different perspectives. To build such a model of object´s size from the real time video data we utilize Gaussians and real-time fast learning algorithm from literature. The built model is used for real-time surveillance systems.
Keywords :
Gaussian processes; calibration; learning (artificial intelligence); real-time systems; video surveillance; Gaussian models; automatic calibration; objects size model; parameter size; real time video data; real-time fast learning algorithm; real-time surveillance systems; video objects; video surveillance systems; Algorithm design and analysis; Europe; Image analysis; Image sequence analysis; Iterative algorithms; Layout; Object detection; Predictive models; Real time systems; Surveillance; Gaussian Models; Machine Learning; Parameter Analysis; Real-Time Applications;
Conference_Titel :
Emerging Technologies & Factory Automation, 2009. ETFA 2009. IEEE Conference on
Conference_Location :
Mallorca
Print_ISBN :
978-1-4244-2727-7
Electronic_ISBN :
1946-0759
DOI :
10.1109/ETFA.2009.5347027
