Vehicle Recognition

Zezhi Chen, Nick Pears, Mike Freeman, Greg Pearce, Jim Austin

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We have worked on roadside vehicle recognition, with a view to embedding this functionality inside roadside ANPR (Automatic Number Plate Recognition) cameras.

Our approach has been to develop methods to detect and segment the vehicle from the rest of the scene using adaptive background segmentation. We then use support vector machine (SVM) classifiers which operate on both the colour of the vehicle to give a colour category classification and the size/shape of the vehicle to give a vehicle type classification, such as 'HGV', 'light van', or 'car'. Other work that we have developed aims to classify the make and model of the vehicle if it is a car.

In the video below (YouTube hosted), the image frame is divide into four quadrants. Top-left shows the raw video stream. Bottom left shows the segmentation. Yellow pixels are the foreground object (the car), green pixels are the car's shadow on the road. Bottom-right shows the car boundary, with shadows removed and top-right shows the segmented car pixels superimposed on top of the learned background and thus the shadow is removed.

Selected publications.

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