A Low False Negative Filter for Detecting Rare Bird Species from Short Video Segments using a Probable Observation Data Set-based EKF Method

Abstract

We report a new filter for assisting the search for rare bird species. Since a rare bird only appears in front of the camera with very low occurrence (e.g. less than ten times per year) for very short duration (e.g. less than a fraction of a second), our algorithm must have very low false negative rate. We verify the bird body axis information with the known bird flying dynamics from the short video segment. Since a regular extended Kalman filter (EKF) cannot converge due to high measurement error and limited data, we develop a novel Probable Observation Data Set (PODS)-based EKF method. The new PODS-EKF searches the measurement error range for all probable observation data that ensures the convergence of the corresponding EKF in short time frame. The algorithm has been extensively tested in experiments. The results show that the algorithm achieves 95.0% area under ROC curve in physical experiment with close to zero false negative rate.