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

fYear

2009

fDate

21-23 May 2009

Firstpage

60

Lastpage

63

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/HSI.2009.5090954

Filename

5090954