Probabilistic Target Detection by Camera-Equipped UAVs

Andrew Symington ( SEP )

This work is motivated by the real world problem of search and rescue by unmanned aerial vehicles (UAVs). We consider the problem of tracking a static target from a bird's-eye view camera mounted to the underside of a quadrotor UAV. We begin by proposing a target detection algorithm, which we then execute on a collection of video frames acquired from four different experiments. We show how the efficacy of the target detection algorithm changes as a function of altitude. We summarise this efficacy into a table which we denote the observation model. We then run the target detection algorithm on a sequence of video frames and use parameters from the observation model to update a recursive Bayesian estimator. The estimator keeps track of the probability that a target is currently in view of the camera, which we refer to more simply as target presence. Between each target detection event the UAV changes position and so the sensing region changes. Under certain assumptions regarding the movement of the UAV, the proportion of new information may be approximated to a value, which we then use to weight the prior in each iteration of the estimator. Through a series of experiments we show how the value of the prior for unseen regions, the altitude of the UAV and the camera sampling rate affect the accuracy of the estimator. Our results indicate that there is no single optimal sampling rate for all tested scenarios. We also show how the prior may be used as a mechanism for tuning the estimator according to whether a high false positive or high false negative probability is preferable.