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所属单位：航天学院

发表刊物：IEEE ACCESS

关键字：Sense and avoid spatiotemporal context fusion conditional random field sparse representation forward and back motion history image

摘要：Deriving from the imperative necessities for developing Sense and Avoid (SAA) capability of Unmanned Aerial Vehicle (UAV), a newly designed flying targets detection algorithm is presented in this paper for enhancing the UAV environment perception ability. Since spatiotemporal context is crucial for insuring the effectiveness of flying targets detection, the algorithm is constructed on the basis of spatiotemporal context fusion. The algorithm proposed in this paper contains three parts, namely the spatial context extraction, temporal context extraction and spatiotemporal context fusion. 1) In order to extract spatial context, dense sampling method is firstly applied to obtain dense image grids, then spatial context is generated via pre-learned conditional random field (CRF) model using a layered structure: dense image patches, bottom feature descriptors, sparse codes, and predicted CRF labels. 2) In order to extract temporal context, the forward and back motion history image (FBMHI) is firstly computed for detecting motion cues, and the adaptive foreground and background isolation is further adopted for acquiring the temporal probability map. 3) The presence probability map of flying targets is finally obtained by spatiotemporal context fusion, and flying targets is therefore picked out by analyzing fused presence probability map. A set of videos containing different drone models are selected for evaluation, and the comparisons against other algorithms demonstrate superiority of the proposed algorithm.

ISSN号：2169-3536

是否译文：否

发表时间：2019-01-01

合写作者：Zhang, Zhouyu,Fan, Yanming,丁萌,Tao, Jiang

通讯作者：曹云峰