What is usual in unusual videos? Trajectory snippet histograms for discovering unusualness
This work addresses the challenge of detecting unusual events in videos, which is important for applications like surveillance and entertainment, though it appears incremental by focusing on patterns in unusual videos rather than usual ones.
The paper tackled the problem of discovering unusual events in videos by identifying shared patterns among unusual videos, proposing a novel descriptor called trajectory snippet histograms. Experiments on domain-specific and unrestricted videos demonstrated its effectiveness in distinguishing unusual videos and locating unusual snapshots.
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.