  Ever-increasing smartphone-generated video content demands intelligent
techniques to edit and enhance videos on power-constrained devices. Most of the
best performing algorithms for video understanding tasks like action
recognition, localization, etc., rely heavily on rich spatio-temporal
representations to make accurate predictions. For effective learning of the
spatio-temporal representation, it is crucial to understand the underlying
object motion patterns present in the video. In this paper, we propose a novel
approach for understanding object motions via motion type classification. The
proposed motion type classifier predicts a motion type for the video based on
the trajectories of the objects present. Our classifier assigns a motion type
for the given video from the following five primitive motion classes: linear,
projectile, oscillatory, local and random. We demonstrate that the
representations learned from the motion type classification generalizes well
for the challenging downstream task of video retrieval. Further, we proposed a
recommendation system for video playback style based on the motion type
classifier predictions.
