  Most object detection methods operate by applying a binary classifier to
sub-windows of an image, followed by a non-maximum suppression step where
detections on overlapping sub-windows are removed. Since the number of possible
sub-windows in even moderately sized image datasets is extremely large, the
classifier is typically learned from only a subset of the windows. This avoids
the computational difficulty of dealing with the entire set of sub-windows,
however, as we will show in this paper, it leads to sub-optimal detector
performance.
  In particular, the main contribution of this paper is the introduction of a
new method, Max-Margin Object Detection (MMOD), for learning to detect objects
in images. This method does not perform any sub-sampling, but instead optimizes
over all sub-windows. MMOD can be used to improve any object detection method
which is linear in the learned parameters, such as HOG or bag-of-visual-word
models. Using this approach we show substantial performance gains on three
publicly available datasets. Strikingly, we show that a single rigid HOG filter
can outperform a state-of-the-art deformable part model on the Face Detection
Data Set and Benchmark when the HOG filter is learned via MMOD.
