3-D model based method for detecting and classifying vehicles in aerial imagery

A computer implemented method for determining a vehicle type of a vehicle detected in an image, comprising the steps of:

• receiving an image comprising a detected vehicle;
• projecting a plurality of three-dimensional vehicle models comprising salient feature locations on the detected vehicle in the image, wherein each vehicle model is associated with a vehicle type;
• comparing a first set of features derived from each of the salient feature locations of the vehicle models to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a plurality of positive match scores (p-scores) and a plurality of negative match scores (n-scores);
• and
• classifying the detected vehicle as one of the plurality of vehicle models based at least in part on the plurality of p-scores and the plurality of n-scores by:
• training a set of specific vehicle-type SVM classifiers;
• comparing the detected vehicle to each of the trained classifiers;
• and
• associating the detected vehicle with a selected trained classifier based on a confidence value produced by the selected trained classifier.

An apparatus for determining a vehicle type of a vehicle detected in an image, comprising:

• an image capture device for receiving an image comprising a detected vehicle;
• and
• a digital processing system configured for:
• projecting a plurality of three-dimensional vehicle models comprising salient feature locations on the detected vehicle in the image, wherein each vehicle model is associated with a vehicle type;
• comparing a first set of features derived from each of the salient feature locations of the vehicle models to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a plurality of positive match scores (p-scores) and a plurality of negative match scores (n-scores);
• and
• classifying the detected vehicle as one of the plurality of vehicle models based at least in part on the plurality of p-scores and the plurality of n-scores by:
• training a set of specific vehicle-type SVM classifiers;
• comparing the detected vehicle to each of the trained classifiers;
• and
• associating the detected vehicle with a selected trained classifier based on a confidence value produced by the selected trained classifier.