Paper Info
Title
Spatio-Temporal MRF model and its Application to Traffic Flow Analyses
Abstract
One of the most important application on Intelligent Transporting System (ITS) is to analyze various traffic activities and construct traffic monitoring system. However, such analyses in previous works have been done by manual inspection to huge amount of traffic images. The major reason why automated analyses of traffic images have been failed is that there does not exist any robust tracking algorithms against such crowded situations at intersections. In order to resolve such a problem, we have developed the tracking algorithm based on Spatio-Temporal Markov Random Field model which is robust against occlusion and clutter problems in 2000. This algorithm is then improved to deal with the problem of illumination variation which is the other dif- ficult problem in computer vision technology. Utilizing this tracking algorithm, an application to acquire traffic flow statistics based on operation hierarchy. This system is able to acquire traffic event statistics such as vehicle counts distinguishing travel directions, velocities, frequent paths and so on.
Year
DOI
Venue
2005
10.1109/ICDE.2005.288
ICDE Workshops
Keywords
Field
DocType
robust tracking algorithm,traffic event statistic,spatio-temporal mrf model,clutter problem,traffic image,important application,traffic monitoring system,traffic flow statistic,traffic flow analyses,ficult problem,various traffic activity,tracking algorithm,algorithm design and analysis,robustness,image analysis,inspection,statistics,computer vision,traffic flow,failure analysis,intelligent systems
Data mining,Traffic flow,Algorithm design,Intelligent decision support system,Clutter,Computer science,Markov random field,Robustness (computer science),Condition monitoring,Hierarchy
Conference
ISBN
Citations 
PageRank 
0-7695-2657-8
0
0.34
References 
Authors
16
1
Name
Order
Citations
PageRank
Shunsuke KAMIJO100.34