Spatio-Temporal MRF model and its Application to Traffic Flow Analyses
- 1 January 2005
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
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.Keywords
This publication has 14 references indexed in Scilit:
- Advanced traffic management system on I-476 in PennsylvaniaPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Recognizing vehicles in infrared images using IMAP parallel vision boardIEEE Transactions on Intelligent Transportation Systems, 2001
- Traffic monitoring and accident detection at intersectionsIEEE Transactions on Intelligent Transportation Systems, 2000
- PicToSeek: combining color and shape invariant features for image retrievalIEEE Transactions on Image Processing, 2000
- Robust tracking of position and velocity with Kalman snakesIeee Transactions On Pattern Analysis and Machine Intelligence, 1999
- Automatic differentiation facilitates OF-integration into steering-angle-based road vehicle trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- 3D Pose Estimation by Directly Matching Polyhedral Models to Gray Value GradientsInternational Journal of Computer Vision, 1997
- Markov random field models for unsupervised segmentation of textured color imagesIeee Transactions On Pattern Analysis and Machine Intelligence, 1995
- Unsupervised texture segmentation using Markov random field modelsIeee Transactions On Pattern Analysis and Machine Intelligence, 1991
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesIeee Transactions On Pattern Analysis and Machine Intelligence, 1984