  We introduce algorithms to visualize feature spaces used by object detectors.
The tools in this paper allow a human to put on `HOG goggles' and perceive the
visual world as a HOG based object detector sees it. We found that these
visualizations allow us to analyze object detection systems in new ways and
gain new insight into the detector's failures. For example, when we visualize
the features for high scoring false alarms, we discovered that, although they
are clearly wrong in image space, they do look deceptively similar to true
positives in feature space. This result suggests that many of these false
alarms are caused by our choice of feature space, and indicates that creating a
better learning algorithm or building bigger datasets is unlikely to correct
these errors. By visualizing feature spaces, we can gain a more intuitive
understanding of our detection systems.
