Vehicle-type identification through automated virtual loop assignment and block-based direction biased motion estimation

This paper presents the concept of automated virtual loop assignment and loop-based motion estimation in vehicle-type identification. A major departure of our method from previous approaches is that the loops are automatically assigned to each lane; the size of virtual loops is much smaller for esti...

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Bibliographic Details
Main Authors: Yung, N.H.C., Chan, K.C., Lai, A.H.S.
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Costs
Deformable models
Intelligent transportation systems
Laser radar
Motion estimation
Roads
Surveillance
Traffic control
Vehicle detection
Online Access:Request full text
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Summary:This paper presents the concept of automated virtual loop assignment and loop-based motion estimation in vehicle-type identification. A major departure of our method from previous approaches is that the loops are automatically assigned to each lane; the size of virtual loops is much smaller for estimation accuracy; and the number of virtual loops per lane is large. Comparing this with traditional ILD, there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy and fully utilize computing resources. Second, there is no failure rate associated with the virtual loops and installation and maintenance cost can be kept to a minimum. Third, virtual loops may be re-allocated anywhere on the frame, giving flexibility in detecting different parameters.
DOI:10.1109/ITSC.1999.821145