Recognizing Cars in Aerial Imagery to Improve Orthophotos

published imagery. Cars are an obvious clutter in a 3D city
model. Therefore we want to detect and remove them.
Our efficient car detector exploits an on-line Boosting algorithm
for training the detector. We use on-line learning with
a human to build a good detector from only a few labeled
samples, and we can successively improve the detector to
achieve satisfaction with the performance. We believe that
our procedure can be applied to a range of different objects,
not just cars. Of urgent interest are people.
In our experimental work, we demonstrated that we can
train the detector with just a few hundred samples. This is
far less than other approaches need, for example [27] report
results with 7800 (positive and negative) examples. We believe
that we can decrease the training effort perhaps to a
level where just two samples are needed.
If we scale up our current results, as obtained from only two
images with 500 cars, to apply to 400 non-redundant images
with 100,000 cars, we would achieve an automated detection
of 95000 cars, we would have to cope with 5000 false detections
(which do not hurt for inpainting) and we would use
about 130 hours of computing (on a single machine). Scaling
the work to use 3000 images for the same area and the
same number of cars will be possible once we have started
to exploit the high overlap imagery.
6.3 Next Steps
Our hope for further improvements is nourished by the availability
of overlapping images, typically 10 images per ground
patch. We envision an approach that automatically improves
the detector in an unsupervised fashion. The image
overlaps will help to obtain additionally positive samples,
e.g. when a car is detected in one image and not in the
other.
Our optimism is further encouraged by recent unpublished
work that employs 3D information from the images to automatically
label the false negatives and use them for training.
We are also exploring co-training strategies by using
extracted height field features to construct a shape-based
car model in an appearance driven detection process.
We speculate that a combination of approaches will achieve a
better performance and also a greater level of generalization
towards use for various objects and types of aerial imagery.
We have recently proposed an approach called “conservative
learning” [21]. There we have demonstrated that by
using a combination of generative and discriminative classifiers
and a conservative update strategy a person detector
can be learned in an unsupervised manner.
7. ACKNOWLEDGMENTS
This work has been supported by the APAFA Project No.
813397, financed by the Austrian Research Promotion Agency
(http://www.ffg.at) and the Virtual Earth Academic Research
Collaboration funded by Microsoft.
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