  Bird strikes present a huge risk for aircraft, especially since traditional
airport bird surveillance is mainly dependent on inefficient human observation.
Computer vision based technology has been proposed to automatically detect
birds, determine bird flying trajectories, and predict aircraft takeoff delays.
However, the characteristics of bird flight using imagery and the performance
of existing methods applied to flying bird task are not well known. Therefore,
we perform infrared flying bird tracking experiments using 12 state-of-the-art
algorithms on a real BIRDSITE-IR dataset to obtain useful clues and recommend
feature analysis. We also develop a Struck-scale method to demonstrate the
effectiveness of multiple scale sampling adaption in handling the object of
flying bird with varying shape and scale. The general analysis can be used to
develop specialized bird tracking methods for airport safety, wildness and
urban bird population studies.
