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题名: Dual-Layer Density Estimation for Multiple Object Instance Detection
作者: Zhang Q(张强); Qu DK(曲道奎); Xu F(徐方); Jia K(贾凯); Sun, Xueying
作者部门: 其他
刊名: JOURNAL OF SENSORS
ISSN号: 1687-725X
出版日期: 2016
页码: 1-12
收录类别: SCI ; EI
产权排序: 1
项目资助者: Shenyang SIASUN Robot Automation Co., Ltd. ; National Key Technology R&D Program, China [2015BAF13B00]
摘要: This paper introduces a dual-layer density estimation-based architecture for multiple object instance detection in robot inventory management applications. The approach consists of raw scale-invariant feature transform (SIFT) feature matching and key point projection. The dominant scale ratio and a reference clustering threshold are estimated using the first layer of the density estimation. A cascade of filters is applied after feature template reconstruction and refined feature matching to eliminate false matches. Before the second layer of density estimation, the adaptive threshold is finalized by multiplying an empirical coefficient for the reference value. The coefficient is identified experimentally. Adaptive threshold-based grid voting is applied to find all candidate object instances. Error detection is eliminated using final geometric verification in accordance with Random Sample Consensus (RANSAC). The detection results of the proposed approach are evaluated on a self-built dataset collected in a supermarket. The results demonstrate that the approach provides high robustness and low latency for inventory management application.
语种: 英语
WOS记录号: WOS:000383462600001
WOS标题词: Science & Technology ; Technology
类目[WOS]: Engineering, Electrical & Electronic ; Instruments & Instrumentation
关键词[WOS]: RECOGNITION ; FEATURES ; SPACE
研究领域[WOS]: Engineering ; Instruments & Instrumentation
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/19239
Appears in Collections:其他_期刊论文

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Recommended Citation:
Zhang Q,Qu DK,Xu F,et al. Dual-Layer Density Estimation for Multiple Object Instance Detection[J]. JOURNAL OF SENSORS,2016:1-12.
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