  We present BEAMER: a new spatially exploitative approach to learning object
detectors which shows excellent results when applied to the task of detecting
objects in greyscale aerial imagery in the presence of ambiguous and noisy
data. There are four main contributions used to produce these results. First,
we introduce a grammar-guided feature extraction system, enabling the
exploration of a richer feature space while constraining the features to a
useful subset. This is specified with a rule-based generative grammar crafted
by a human expert. Second, we learn a classifier on this data using a newly
proposed variant of AdaBoost which takes into account the spatially correlated
nature of the data. Third, we perform another round of training to optimize the
method of converting the pixel classifications generated by boosting into a
high quality set of (x, y) locations. Lastly, we carefully define three common
problems in object detection and define two evaluation criteria that are
tightly matched to these problems. Major strengths of this approach are: (1) a
way of randomly searching a broad feature space, (2) its performance when
evaluated on well-matched evaluation criteria, and (3) its use of the location
prediction domain to learn object detectors as well as to generate detections
that perform well on several tasks: object counting, tracking, and target
detection. We demonstrate the efficacy of BEAMER with a comprehensive
experimental evaluation on a challenging data set.
