A Comparative Study of Object Trackers for Infrared Flying Bird Tracking
This work tackles the problem of improving airport safety and bird population studies through automated tracking, but it is incremental as it primarily evaluates and adapts existing methods.
The study compared 12 state-of-the-art object trackers on an infrared dataset to analyze their performance in tracking flying birds, aiming to address the lack of knowledge about bird flight characteristics in imagery and existing methods, and developed a Struck-scale method to handle varying shapes and scales.
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.