  Unusual events are important as being possible indicators of undesired
consequences. Moreover, unusualness in everyday life activities may also be
amusing to watch as proven by the popularity of such videos shared in social
media. Discovery of unusual events in videos is generally attacked as a problem
of finding usual patterns, and then separating the ones that do not resemble to
those. In this study, we address the problem from the other side, and try to
answer what type of patterns are shared among unusual videos that make them
resemble to each other regardless of the ongoing event. With this challenging
problem at hand, we propose a novel descriptor to encode the rapid motions in
videos utilizing densely extracted trajectories. The proposed descriptor, which
is referred to as trajectory snipped histograms, is used to distinguish unusual
videos from usual videos, and further exploited to discover snapshots in which
unusualness happen. Experiments on domain specific people falling videos and
unrestricted funny videos show the effectiveness of our method in capturing
unusualness.
