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
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
object detection , background subtraction , video signal processing , image classification , image motion analysis , vehicle type , Gaussian processes , closed circuit television , Gaussian mixture model-support vector machine approach , multidimensional smoothing transform , colour video data , colour classification , support vector machines , pragmatic vehicle classification applications , kernelised support vector machine , cameras , transforms , camera vibration , image segmentation , traffic engineering computing , moving road vehicle segmentation , stationary roadside CCTV camera , shadow detection approach , image colour analysis
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