  We propose an unsupervised approach for discovering characteristic motion
patterns in videos of highly articulated objects performing natural, unscripted
behaviors, such as tigers in the wild. We discover consistent patterns in a
bottom-up manner by analyzing the relative displacements of large numbers of
ordered trajectory pairs through time, such that each trajectory is attached to
a different moving part on the object. The pairs of trajectories descriptor
relies entirely on motion and is more discriminative than state-of-the-art
features that employ single trajectories. Our method generates temporal video
intervals, each automatically trimmed to one instance of the discovered
behavior, and clusters them by type (e.g., running, turning head, drinking
water). We present experiments on two datasets: dogs from YouTube-Objects and a
new dataset of National Geographic tiger videos. Results confirm that our
proposed descriptor outperforms existing appearance- and trajectory-based
descriptors (e.g., HOG and DTFs) on both datasets and enables us to segment
unconstrained animal video into intervals containing single behaviors.
