  With the advent of drones, aerial video analysis becomes increasingly
important; yet, it has received scant attention in the literature. This paper
addresses a new problem of parsing low-resolution aerial videos of large
spatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigning
roles to people engaged in events. We propose a novel framework aimed at
conducting joint inference of the above tasks, as reasoning about each in
isolation typically fails in our setting. Given noisy tracklets of people and
detections of large objects and scene surfaces (e.g., building, grass), we use
a spatiotemporal AND-OR graph to drive our joint inference, using Markov Chain
Monte Carlo and dynamic programming. We also introduce a new formalism of
spatiotemporal templates characterizing latent sub-events. For evaluation, we
have collected and released a new aerial videos dataset using a hex-rotor
flying over picnic areas rich with group events. Our results demonstrate that
we successfully address above inference tasks under challenging conditions.
