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With the advent of drones, aerial video analysis becomes increasinglyimportant; yet, it has received scant attention in the literature. This paperaddresses a new problem of parsing low-resolution aerial videos of largespatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigningroles to people engaged in events. We propose a novel framework aimed atconducting joint inference of the above tasks, as reasoning about each inisolation typically fails in our setting. Given noisy tracklets of people anddetections of large objects and scene surfaces (e.g., building, grass), we usea spatiotemporal AND-OR graph to drive our joint inference, using Markov ChainMonte Carlo and dynamic programming. We also introduce a new formalism ofspatiotemporal templates characterizing latent sub-events. For evaluation, wehave collected and released a new aerial videos dataset using a hex-rotorflying over picnic areas rich with group events. Our results demonstrate thatwe successfully address above inference tasks under challenging conditions.

Joint Inference of Groups, Events and Human Roles in Aerial Videos