CN112113572A - 一种解决分布式标签融合的多目标跟踪方法 - Google Patents
一种解决分布式标签融合的多目标跟踪方法 Download PDFInfo
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- CN112113572A CN112113572A CN202010987602.6A CN202010987602A CN112113572A CN 112113572 A CN112113572 A CN 112113572A CN 202010987602 A CN202010987602 A CN 202010987602A CN 112113572 A CN112113572 A CN 112113572A
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Abstract
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目标 | 出生位置 | 出生时间(s) | 死亡时间(s) |
目标1 | [1003.86,-1488.26] | 1 | 100 |
目标2 | [-255.88,1011.41] | 10 | 100 |
目标3 | [-1507.38,256.79] | 10 | 50 |
目标4 | [246.26,738.93] | 15 | 80 |
目标5 | [-1500,250] | 20 | 66 |
目标6 | [-242.62,993.21] | 30 | 100 |
目标7 | [-250,980] | 40 | 80 |
目标8 | [992,1500] | 40 | 100 |
目标9 | [1000,1500] | 60 | 100 |
目标10 | [250,750] | 60 | 100 |
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112325880A (zh) * | 2021-01-04 | 2021-02-05 | 中国人民解放军国防科技大学 | 分布式平台相对定位方法、装置、计算机设备和存储介质 |
CN113219452A (zh) * | 2021-05-07 | 2021-08-06 | 电子科技大学 | 未知视域下的分布式多雷达联合配准与多目标跟踪方法 |
CN114089363A (zh) * | 2021-11-16 | 2022-02-25 | 哈尔滨工程大学 | 一种基于随机有限集的异构传感器信息融合和多目标跟踪方法 |
CN115099343A (zh) * | 2022-06-24 | 2022-09-23 | 江南大学 | 一种有限视野下分布式标签多伯努利融合跟踪方法 |
Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2009103046A2 (en) * | 2008-02-14 | 2009-08-20 | The Penn State Research Foundation | Medical image reporting system and method |
CN106291533A (zh) * | 2016-07-27 | 2017-01-04 | 电子科技大学 | 一种基于amd的分布式多传感器融合算法 |
CN106384121A (zh) * | 2016-08-30 | 2017-02-08 | 电子科技大学 | 基于标号空间匹配的标号随机集滤波器分布式融合方法 |
CN106443632A (zh) * | 2016-12-01 | 2017-02-22 | 西安电子科技大学 | 基于标签保持多任务因子分析模型的雷达目标识别方法 |
CN106910205A (zh) * | 2017-03-03 | 2017-06-30 | 深圳市唯特视科技有限公司 | 一种基于随机有限集滤波器耦合的多目标跟踪方法 |
CN107102295A (zh) * | 2017-04-13 | 2017-08-29 | 杭州电子科技大学 | 基于glmb滤波的多传感器tdoa无源定位方法 |
CN107423539A (zh) * | 2017-03-29 | 2017-12-01 | 中国电子科技集团公司第三十八研究所 | 一种gm‑phd滤波器的标签分配方法 |
CN109508444A (zh) * | 2018-12-18 | 2019-03-22 | 桂林电子科技大学 | 区间量测下交互式多模广义标签多伯努利的快速跟踪方法 |
CN110033089A (zh) * | 2019-04-17 | 2019-07-19 | 山东大学 | 基于分布式估计算法的深度神经网络参数优化方法及系统 |
WO2020007487A1 (en) * | 2018-07-06 | 2020-01-09 | Bayerische Motoren Werke Aktiengesellschaft | Object tracking based on multiple measurement hypotheses |
US20200134827A1 (en) * | 2018-10-26 | 2020-04-30 | Samsung Electronics Co., Ltd. | Method and apparatus for image segmentation using an event sensor |
-
2020
- 2020-09-18 CN CN202010987602.6A patent/CN112113572B/zh active Active
Patent Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2009103046A2 (en) * | 2008-02-14 | 2009-08-20 | The Penn State Research Foundation | Medical image reporting system and method |
US20170345155A1 (en) * | 2008-02-14 | 2017-11-30 | The Penn State Research Foundation | Medical image reporting system and method |
CN106291533A (zh) * | 2016-07-27 | 2017-01-04 | 电子科技大学 | 一种基于amd的分布式多传感器融合算法 |
CN106384121A (zh) * | 2016-08-30 | 2017-02-08 | 电子科技大学 | 基于标号空间匹配的标号随机集滤波器分布式融合方法 |
CN106443632A (zh) * | 2016-12-01 | 2017-02-22 | 西安电子科技大学 | 基于标签保持多任务因子分析模型的雷达目标识别方法 |
CN106910205A (zh) * | 2017-03-03 | 2017-06-30 | 深圳市唯特视科技有限公司 | 一种基于随机有限集滤波器耦合的多目标跟踪方法 |
CN107423539A (zh) * | 2017-03-29 | 2017-12-01 | 中国电子科技集团公司第三十八研究所 | 一种gm‑phd滤波器的标签分配方法 |
CN107102295A (zh) * | 2017-04-13 | 2017-08-29 | 杭州电子科技大学 | 基于glmb滤波的多传感器tdoa无源定位方法 |
WO2020007487A1 (en) * | 2018-07-06 | 2020-01-09 | Bayerische Motoren Werke Aktiengesellschaft | Object tracking based on multiple measurement hypotheses |
US20200134827A1 (en) * | 2018-10-26 | 2020-04-30 | Samsung Electronics Co., Ltd. | Method and apparatus for image segmentation using an event sensor |
CN109508444A (zh) * | 2018-12-18 | 2019-03-22 | 桂林电子科技大学 | 区间量测下交互式多模广义标签多伯努利的快速跟踪方法 |
CN110033089A (zh) * | 2019-04-17 | 2019-07-19 | 山东大学 | 基于分布式估计算法的深度神经网络参数优化方法及系统 |
Non-Patent Citations (6)
Title |
---|
LI MIAO等: ""Track-before-detect for maneuvering small targets based on Labeled Multi-Bernoulli filter"", 《JOURNAL OF INFRARED AND MILLIMETER WAVES》 * |
WEN, LI等: ""Labeling the nucleocapsid of enveloped baculovirus with quantum dots for single-virus tracking"", 《BIOMATERIALS》 * |
XUHUI FAN等: ""Learning Nonparametric Relational Models by Conjugately Incorporating Node Information in a Network"", 《IEEE TRANSACTIONS ON CYBERNETICS》 * |
彭华甫,等: ""标签多伯努利机动目标跟踪与分类算法"", 《西安交通大学学报》 * |
李翠芸,等: ""自适应目标新生δ广义标签多伯努利滤波算法"", 《西安电子科技大学学报》 * |
蔡如华,等: ""弱目标箱粒子标签多伯努利多目标检测与跟踪算法"", 《红外与毫米波学报》 * |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112325880A (zh) * | 2021-01-04 | 2021-02-05 | 中国人民解放军国防科技大学 | 分布式平台相对定位方法、装置、计算机设备和存储介质 |
CN112325880B (zh) * | 2021-01-04 | 2021-03-26 | 中国人民解放军国防科技大学 | 分布式平台相对定位方法、装置、计算机设备和存储介质 |
CN113219452A (zh) * | 2021-05-07 | 2021-08-06 | 电子科技大学 | 未知视域下的分布式多雷达联合配准与多目标跟踪方法 |
CN113219452B (zh) * | 2021-05-07 | 2022-05-31 | 电子科技大学 | 未知视域下的分布式多雷达联合配准与多目标跟踪方法 |
CN114089363A (zh) * | 2021-11-16 | 2022-02-25 | 哈尔滨工程大学 | 一种基于随机有限集的异构传感器信息融合和多目标跟踪方法 |
CN115099343A (zh) * | 2022-06-24 | 2022-09-23 | 江南大学 | 一种有限视野下分布式标签多伯努利融合跟踪方法 |
CN115099343B (zh) * | 2022-06-24 | 2024-03-29 | 江南大学 | 一种有限视野下分布式标签多伯努利融合跟踪方法 |
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