Open AccessDetection of vehicle wheels from images using a pseudo‐wavelet filter for analysis of congested traffic
Author(s) -
OBrien Eugene J.,
Caprani Colin C.,
Blacoe Serena,
Guo Dong,
Malekjafarian Abdollah
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5369
Subject(s) - axle , hough transform , computer science , filter (signal processing) , artificial intelligence , computer vision , matching (statistics) , process (computing) , set (abstract data type) , wavelet transform , wavelet , image (mathematics) , template matching , image processing , pattern recognition (psychology) , traffic congestion , engineering , mathematics , transport engineering , statistics , programming language , operating system , mechanical engineering
There is potential for significant savings if the safety of existing bridges can be more accurately assessed. For long‐span bridges, congestion is the governing traffic load condition. The current methods of simulating congestion make assumptions about the axle‐to‐axle gaps maintained between vehicles. There is potential for improvement in congestion models if accurate data on axle‐to‐axle gaps can be obtained. In this study, the use of a camera to collect this information is put forward. A new image processing technique is proposed to detect wheels in variable light conditions. The method is based on a pseudo‐wavelet filter that amplifies circles, in conjunction with an algorithm that weights features in the image according to their circularity. This new approach is compared with the Hough transform, template matching and the deformable part‐based model (DPM) methods previously developed. In a sample set of 80 images, 96.9% of wheels are detected, considerably more than with the Hough transform and template matching methods. It also provides the same level of accuracy as DPM without requiring a training process.