The authors describe their approach to segmenting moving road vehicles from the colour video data supplied by a stationary roadside closed‐circuit television (CCTV) camera and classifying those vehicles in terms of type (car, van and heavy goods vehicle) and dominant colour. For the segmentation, the authors use a recursively updated Gaussian mixture model approach, with a multi‐dimensional smoothing transform. The authors show that this transform improves the segmentation performance, particularly in adverse imaging conditions, such as when there is camera vibration. The authors then present a comprehensive comparative evaluation of shadow detection approaches, which is an essential component of background subtraction in outdoor scenes. For vehicle classification, a practical and systematic approach using a kernelised support vector machine is developed. The good recognition rates achieved in the authors’ experiments indicate that their approach is well suited for pragmatic vehicle classification applications.
A Gaussian mixture model and support vector machine approach to vehicle type and colour classification
IET Intelligent Transport Systems ; 8 , 2 ; 135-144
2014-03-01
10 pages
Article (Journal)
Electronic Resource
English
colour video data , image segmentation , cameras , image colour analysis , pragmatic vehicle classification applications , image motion analysis , object detection , stationary roadside CCTV camera , transforms , camera vibration , background subtraction , moving road vehicle segmentation , closed circuit television , traffic engineering computing , vehicle type , colour classification , multidimensional smoothing transform , shadow detection approach , video signal processing , support vector machines , Gaussian mixture model‐support vector machine approach , kernelised support vector machine , image classification , Gaussian processes
Intelligent Traffic Monitoring Systems: Vehicle Type Classification Using Support Vector Machine
BASE | 2021
|Taylor & Francis Verlag | 2012
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