Hierarchical incorporation of shape and shape dynamics for flying bird detection

doi:10.1016/j.neucom.2013.10.026

OPT OpenIR > 光谱成像技术研究室

	Hierarchical incorporation of shape and shape dynamics for flying bird detection
	Zhang, Jun 1; Xu, Qunyu 1; Cao, Xianbin 1; Yan, Pingkun 2; Li, Xuelong2
作者部门	光学影像学习与分析中心
	2014-05-05
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	131 页码:179-190
摘要	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. (C) 2013 Elsevier B.V. All rights reserved.
文章类型	Article
关键词	Bird-aircraft Collisions Flying Bird Detection Hierarchical Feature Shape Similarity Shape Dynamics
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2013.10.026
收录类别	SCI ; EI
关键词[WOS]	RECOGNITION
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000332805700020
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/22399
专题	光谱成像技术研究室
作者单位	1.Beihang Univ, Sch Elect & Informat Engn, Natl Key Lab CNS ATM, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shanxi, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Jun,Xu, Qunyu,Cao, Xianbin,et al. Hierarchical incorporation of shape and shape dynamics for flying bird detection[J]. NEUROCOMPUTING,2014,131:179-190.
APA	Zhang, Jun,Xu, Qunyu,Cao, Xianbin,Yan, Pingkun,&Li, Xuelong.(2014).Hierarchical incorporation of shape and shape dynamics for flying bird detection.NEUROCOMPUTING,131,179-190.
MLA	Zhang, Jun,et al."Hierarchical incorporation of shape and shape dynamics for flying bird detection".NEUROCOMPUTING 131(2014):179-190.

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