Automatic Bird Species Filtering Using a Multimodel Approach

Abstract

We report a filtering algorithm for bird species detection using videos captured by uncalibrated moving cameras, a typical characteristic of crowd sourced videos. The algorithm tracks both body and wing motion dimensions of a flying bird to form signatures for species filtering. In the body model, we consider both cases when background motion introduced by the camera can and cannot be directly recognized using key point matching. We are also able to recover intrinsic camera parameters in the body motion tracking. In the wing model, we consider both periodic wing flapping and gliding motion patterns. These models are combined to form a multimodel framework. We have tested the algorithm and compared its performance with single model approaches in physical experiments. Results show that the new algorithm significantly reduces the false positive rate, while maintaining a low false negative rate. The area under ROC curve is 92.86%.