A Gaussian mixture model and support vector machine approach to vehicle type and colour classification
Zezhi Chen, Nick E. Pears, Michael Freeman, Jim Austin · IET Intelligent Transport Systems · 2013
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.