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- W634802479 abstract "One of the most important application on intelligent transportation systems (ITS) is to analyze various traffic activities and construct traffic monitoring system. However, such analyses in previous works have been done by manual investigation to huge amount of traffic images. The major reason why automated analyses of traffic images have failed is that there do not exist any robust tracking algorithms against such crowded situations at intersections. In order to resolve such a problem, the authors have developed the tracking algorithm based on Spatio-Temporal Markov Random Field model which is robust against occlusion and clutter problems in 2000. Since the algorithm is able to track each individual vehicle even in cases of heavy occlusion situations in crowded traffic, this enables automated acquisition of detailed information from traffic images. In this paper the authors constructed an automated system which is able to acquire traffic event statistics such as vehicle counts distinguishing travel directions, velocities, frequent paths and so on. By examining this system using traffic images of 2 hours sampled from 24 hours images, this system was able to count number of vehicles in 95% success rate. The major reason for the failure was that some vehicles moving together were recognized as one. In addition, this system was able to detect the moment of signal changing by observing average velocities of vehicles. All these automation systems are very useful, for they can save large amount of human workload." @default.
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- W634802479 date "2001-01-01" @default.
- W634802479 modified "2023-09-24" @default.
- W634802479 title "AUTOMATED STATISTICS ACQUISITION FROM TRAFFIC IMAGES BASED ON OCCLUSION ROBUST VEHICLE TRACKING METHOD" @default.
- W634802479 hasPublicationYear "2001" @default.
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