A revisit-based mixed-initiative nested classification scheme for Unmanned Aerial Vehicles
Yash Chitalia, Weijia Zhang, Baro Hyun, Anouck Girard · 2014
Unmanned Aerial Vehicles (UAVs), used often by the Armed Forces for Surveillance and Reconnaissance (S&R) missions, are powerful classification agents to inspect objects of interest (OOIs) under human supervision. To achieve improved decision-making, we have previously explored the idea of a two-tiered classification structure, where a primary trichotomous classifier (machine) precedes a secondary dichotomous classifier (human). The trend for future operations is for a single operator to control an increasing number of UAVs. However, low human-to-UAV ratio can result in a stressful situation for the human operator, which is undesirable for successful classification and UAV management. To address this issue, we extend our previous work to a three-tiered classification scheme, where an intermediate revisit sensor makes a decision to revisit the OOI in cases where the primary classifier is unsure, which can be caused by noisy sensor data or viewing from a poor vantage point. We compare the performance (i.e., the probability of misclassification) under single, two-tiered, and three-tiered classifier schemes and show the efficacy of the proposed technique.