Open AccessA Gaussian mixture model and support vector machine approach to vehicle type and colour classification
Author(s) -
Chen Zezhi,
Pears Nick,
Freeman Michael,
Austin Jim
Publication year - 2014
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2012.0104
Subject(s) - background subtraction , artificial intelligence , mixture model , segmentation , support vector machine , computer vision , computer science , shadow (psychology) , smoothing , pattern recognition (psychology) , component (thermodynamics) , market segmentation , image segmentation , pixel , psychology , physics , psychotherapist , thermodynamics , marketing , business
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.