Chen Chen, Pawel W. Wozniak, Andrzej Romanowski, Mohammad Obaid, Tomasz Jaworski, Jacek Kucharski, Krzysztof Grudzien, Shengdong Zhao, Morten Fjeld
In this article, we present a novel application domain for human computation, specifically for crowdsourcing, which can help in understanding particle-tracking problems. Through an interdisciplinary inquiry, we built a crowdsourcing system designed to detect tracer particles in industrial tomographic images, and applied it to the problem of bulk solid flow in silos. As images from silo-sensing systems cannot be adequately analysed using the currently available computational methods, human intelligence is required. However, limited availability of experts, as well as their high cost, motivates employing additional non experts. We report on the results of a study that assesses the task completion time and accuracy of employing nonexpert workers to process large datasets of images in order to generate data for bulk flow research. We prove the feasibility of this approach by comparing results from a user study with data generated from a computational algorithm. The study shows that the crowd is more scalable and more economical than an automatic solution. The system can help analyse and understand the physics of flow phenomena to better inform the future design of silos, and is generalised enough to be applicable to other domains.