A Gaussian mixture model and support vector machine approach to vehicle type and colour classification
- Author(s): Zezhi Chen 1 ; Nick Pears 2 ; Michael Freeman 2 ; Jim Austin 2
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View affiliations
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Affiliations:
1:
Cybula Ltd., Computer Science Building, Deramore Lane, York, YO10 5DD, UK;
2: Department of Computer Science, University of York, York, YO10 5DD, UK
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Affiliations:
1:
Cybula Ltd., Computer Science Building, Deramore Lane, York, YO10 5DD, UK;
- Source:
Volume 8, Issue 2,
March 2014,
p.
135 – 144
DOI: 10.1049/iet-its.2012.0104 , Print ISSN 1751-956X, Online ISSN 1751-9578
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.
Inspec keywords: Gaussian processes; traffic engineering computing; object detection; video signal processing; support vector machines; cameras; image segmentation; image motion analysis; transforms; image colour analysis; image classification; closed circuit television
Other keywords: moving road vehicle segmentation; colour classification; pragmatic vehicle classification applications; multidimensional smoothing transform; kernelised support vector machine; vehicle type; stationary roadside CCTV camera; shadow detection approach; Gaussian mixture model-support vector machine approach; colour video data; camera vibration; background subtraction
Subjects: Other topics in statistics; Video signal processing; Computer vision and image processing techniques; Optical, image and video signal processing; Integral transforms; Integral transforms; Traffic engineering computing; Other topics in statistics; Knowledge engineering techniques
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