A Comparative Study of Object Trackers for Infrared Flying Bird Tracking

TL;DR

This paper compares 12 advanced algorithms for infrared flying bird tracking, analyzing their performance and proposing a multi-scale sampling method to improve tracking accuracy for airport safety and ecological studies.

Contribution

It provides a comprehensive evaluation of existing trackers on a real dataset and introduces a novel multi-scale sampling approach for better bird flight tracking.

Findings

Multi-scale sampling improves tracking robustness.

Infrared bird tracking performance varies across algorithms.

The study offers insights for developing specialized bird tracking methods.

Abstract

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.