CrowdAR: a live video annotation tool for rapid mapping

Digital Humanitarians are a powerful and effective resource to analyse the vast amounts of data that disasters generate. Aerial vehicles are increasingly being used for gathering high resolution imagery of affected areas, but require a lot of effort to effectively analyse, typically taking days to complete. We introduce CrowdAR, a real-time crowdsourcing platform that tags live footage from aerial vehicles flown during disasters. CrowdAR enables the analysis of footage within minutes, can rapidly plot snippets of the video onto a map, and can reduce the cognitive load of pilots by augmenting their live video feed with crowd annotations

Observation modelling for vision-based target search by unmanned aerial vehicles

Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.Unmanned Aerial Vehicles (UAVs) are playing an increasing role in gathering information about objects on the ground. In particular, a key problem is to detect and classify objects from a sequence of camera images. However, existing systems typically adopt an idealised model of sensor observations, by assuming they are independent, and take the form of maximum likelihood predictions of an objects class. In contrast, real vision systems produce output that can be highly correlated and corrupted by noise. Therefore, traditional approaches can lead to inaccurate or overconfident results, which in turn lead to poor decisions about what to observe next to improve these predictions. To address these issues, we develop a Gaussian Process based observation model that characterises the correlation between classifier outputs as a function of UAV position. We then use this to fuse classifier observations from a sequence of images and to plan the UAVs movements. In both real and simulated target search scenarios, we show that this can achieve a decrease in mean squared detection error of up to 66% relative to existing state-of-the-art methods