Accurate Segmentation for Infrared Flying Bird Tracking. Issue 4 (1st July 2016)

• Record Type: Journal Article
• Authors: Zheng, Hong; Huang, Ying; Ling, Haibin; Zou, Qi; Yang, Hao
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 4(2016)
• Page Start: 625
• Page End: 631
• Publication Date: 2016-07-01
• Subjects: Object tracking -- Edge detection -- Segmentation -- Gaussian mixture model -- Markov random field (MRF); computer vision -- Gaussian processes -- image segmentation -- Markov processes -- object tracking; infrared flying bird tracking -- traditional airport bird surveillance -- computer vision techniques -- bird monitoring -- segmentation based approach -- object localization method -- Markov random field -- background mixture Gaussian models -- foreground mixture Gaussian models; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.07.008
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : Bird strikes present a huge risk for air vehicles, especially since traditional airport bird surveillance is mainly dependent on inefficient human observation. For improving the effectiveness and efficiency of bird monitoring, computer vision techniques have been proposed to detect birds, determine bird flying trajectories, and predict aircraft takeoff delays. Flying bird with a huge deformation causes a great challenge to current tracking algorithms. We propose a segmentation based approach to enable tracking can adapt to the varying shape of bird. The approach works by segmenting object at a region of interest, where is determined by the object localization method and heuristic edge information. The segmentation is performed by Markov random field, which is trained by foreground and background mixture Gaussian models. Experiments demonstrate that the proposed approach provides the ability to handle large deformations and outperforms the most state‐of‐the‐art tracker in the infrared flying bird tracking problem.