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

A Comparative Study of Object Trackers for Infrared Flying Bird Tracking