A monocular vision-based low false negative filter for assisting the search for rare bird species using a probable observation data set-based EKF method

To assist nature observation, we take on the challenge of search for rare bird species using a single fixed camera. To reduce the huge amount of data for identification, we develop a model-based filtering approach that verifies the bird body axis information with the known bird flying dynamics. As a commonly used method, an extended Kalman filter (EKF) cannot be directly applied because the EKF would not converge due to the high measurement error introduced by image segmentation and the limited observation data due to the high flying speed of the bird. To cope with the problem, we develop a novel Probable Observation Data Set (PODS)-based EKF method. The novel PODS-EKF searches the measurement error range for all probable observation data that ensures the convergence of the corresponding EKF. The filtering is based on whether the set PODS is non-empty and the corresponding velocity is within the known bird flying velocity profile. The algorithm has been extensively tested using both simulated inputs and physical experiments. The results show that the algorithm can reduce the video data for identification by over 99.99936% with close to zero false negative.