Paper Info
Title
Traffic monitoring and accident detection at intersections
Abstract
We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness
Year
DOI
Venue
2000
10.1109/6979.880968
IEEE Transactions on Intelligent Transportation Systems
Keywords
Field
DocType
accidents,computer vision,computerised monitoring,feature extraction,hidden markov models,learning systems,optical tracking,road traffic,markov random field,accident detection,hidden markov model,intersections,learning system,tracking,traffic monitoring,pixel,computer algorithms,robustness,image segmentation,image processing,tracking system
Computer vision,Markov random field,Markov model,Simulation,Image processing,Tracking system,Feature extraction,Artificial intelligence,Pixel,Engineering,Hidden Markov model,Jamming
Journal
Volume
Issue
ISSN
1
2
1524-9050
Citations 
PageRank 
References 
173
13.74
14
Authors
4
Search Limit
100173
Name
Order
Citations
PageRank
Kamijo, S.117313.74
Yasuyuki Matsushita22046113.32
Ikeuchi, K.320320.27
Masao Sakauchi4688149.27