A 3D Teacher for Car Detection in Aerial Images

Proceedings of the Eleventh IEEE International Conference on Computer Vision,
Workshop on 3D Representation for Recognition (3dRR-07)

This paper demonstrates how to reduce the hand labeling
effort considerably by 3D information in an object detection
task. In particular, we demonstrate how an efficient
car detector for aerial images with minimal hand labeling
effort can be build. We use an on-line boosting algorithm
to incrementally improve the detection results. Initially,
we train the classifier with a single positive (car) example,
randomly drawn from a fixed number of given samples.
When applying this detector to an image we obtain many
false positive detections. We use information from a stereo
matcher to detect some of these false positives (e.g. detected
cars on a facade) and feed back this information to the classifier
as negative updates. This improves the detector considerably,
thus reducing the number of false positives. We
show that we obtain similar results to hand labeling by iteratively
applying this strategy. The performance of our algorithm
is demonstrated on digital aerial images of urban
environments.

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