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• W2000766642 abstract "Flying bird detection (FBD) is critical in avoiding bird–aircraft collisions. Most existing approaches rely on motion detection to identify the flying bird, since it is a typical moving object. However, when there exist other moving objects, those methods often fail to distinguish flying birds from those objects due to the insufficiency of feature description. In this paper, we introduce a novel hierarchical feature model exploiting shape and shape dynamics to improve the ability of representing a flying bird, and then apply it to the FBD problem. As the shape of a flying bird is very distinctive in geometric structures and could provide discriminating spatial information, an improved shape context feature descriptor is proposed at the lower level to capture the spatial relations in bird shape. Then the shape descriptor is extended into the spatio-temporal domain and a shape dynamics description is built at the higher level, in which a 4-state Markovian model is adopted and is learned from training sequences. Moreover, to build a mapping from the lower level to the higher level of the hierarchy, a shape similarity index (SSI) based matching mechanism is designed. We apply these two-level features for detecting flying bird for improved safety of aircrafts flying at low-altitude. The experimental results show that the proposed method is effective and outperforms three other existing vision-based FBD approaches." @default.
• W2000766642 created "2016-06-24" @default.
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• W2000766642 date "2014-05-01" @default.
• W2000766642 modified "2023-10-11" @default.
• W2000766642 title "Hierarchical incorporation of shape and shape dynamics for flying bird detection" @default.
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• W2000766642 doi "https://doi.org/10.1016/j.neucom.2013.10.026" @default.
• W2000766642 hasPublicationYear "2014" @default.
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