CN113255611A - 基于动态标签分配的孪生网络目标跟踪方法及移动设备 - Google Patents
基于动态标签分配的孪生网络目标跟踪方法及移动设备 Download PDFInfo
- Publication number
- CN113255611A CN113255611A CN202110754387.XA CN202110754387A CN113255611A CN 113255611 A CN113255611 A CN 113255611A CN 202110754387 A CN202110754387 A CN 202110754387A CN 113255611 A CN113255611 A CN 113255611A
- Authority
- CN
- China
- Prior art keywords
- sample feature
- tracking
- twin network
- dynamic label
- feature points
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 230000004044 response Effects 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 6
- 238000004364 calculation method Methods 0.000 claims abstract description 4
- 238000000605 extraction Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 description 14
- 238000012549 training Methods 0.000 description 14
- 238000004422 calculation algorithm Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 238000005457 optimization Methods 0.000 description 6
- 230000000007 visual effect Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 230000001788 irregular Effects 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110754387.XA CN113255611B (zh) | 2021-07-05 | 2021-07-05 | 基于动态标签分配的孪生网络目标跟踪方法及移动设备 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110754387.XA CN113255611B (zh) | 2021-07-05 | 2021-07-05 | 基于动态标签分配的孪生网络目标跟踪方法及移动设备 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113255611A true CN113255611A (zh) | 2021-08-13 |
CN113255611B CN113255611B (zh) | 2021-10-01 |
Family
ID=77190696
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110754387.XA Active CN113255611B (zh) | 2021-07-05 | 2021-07-05 | 基于动态标签分配的孪生网络目标跟踪方法及移动设备 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113255611B (zh) |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113838099A (zh) * | 2021-10-20 | 2021-12-24 | 浙江大立科技股份有限公司 | 一种基于孪生神经网络的单目标跟踪方法 |
CN113870330A (zh) * | 2021-09-30 | 2021-12-31 | 四川大学 | 基于特定标签和损失函数的孪生视觉跟踪方法 |
CN114299113A (zh) * | 2021-12-27 | 2022-04-08 | 北京航空航天大学 | 一种基于孪生网络的目标跟踪方法及装置 |
CN114529583A (zh) * | 2022-02-10 | 2022-05-24 | 国网河南省电力公司电力科学研究院 | 基于残差回归网络的电力设备跟踪方法及跟踪系统 |
CN114820712A (zh) * | 2022-05-16 | 2022-07-29 | 太原科技大学 | 一种自适应目标框优化的无人机跟踪方法 |
CN115761393A (zh) * | 2022-10-18 | 2023-03-07 | 北京航空航天大学 | 一种基于模板在线学习的无锚目标跟踪方法 |
WO2023207742A1 (zh) * | 2022-04-28 | 2023-11-02 | 南京理工大学 | 一种交通异常行为检测方法与系统 |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110222792A (zh) * | 2019-06-20 | 2019-09-10 | 杭州电子科技大学 | 一种基于孪生网络的标签缺陷检测算法 |
CN111179307A (zh) * | 2019-12-16 | 2020-05-19 | 浙江工业大学 | 一种全卷积分类及回归孪生网络结构的视觉目标跟踪方法 |
US10733755B2 (en) * | 2017-07-18 | 2020-08-04 | Qualcomm Incorporated | Learning geometric differentials for matching 3D models to objects in a 2D image |
CN111627050A (zh) * | 2020-07-27 | 2020-09-04 | 杭州雄迈集成电路技术股份有限公司 | 一种目标跟踪模型的训练方法和装置 |
CN112966553A (zh) * | 2021-02-02 | 2021-06-15 | 同济大学 | 基于孪生网络的强耦合目标跟踪方法、装置、介质及设备 |
-
2021
- 2021-07-05 CN CN202110754387.XA patent/CN113255611B/zh active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10733755B2 (en) * | 2017-07-18 | 2020-08-04 | Qualcomm Incorporated | Learning geometric differentials for matching 3D models to objects in a 2D image |
CN110222792A (zh) * | 2019-06-20 | 2019-09-10 | 杭州电子科技大学 | 一种基于孪生网络的标签缺陷检测算法 |
CN111179307A (zh) * | 2019-12-16 | 2020-05-19 | 浙江工业大学 | 一种全卷积分类及回归孪生网络结构的视觉目标跟踪方法 |
CN111627050A (zh) * | 2020-07-27 | 2020-09-04 | 杭州雄迈集成电路技术股份有限公司 | 一种目标跟踪模型的训练方法和装置 |
CN112966553A (zh) * | 2021-02-02 | 2021-06-15 | 同济大学 | 基于孪生网络的强耦合目标跟踪方法、装置、介质及设备 |
Non-Patent Citations (1)
Title |
---|
DAWEI ZHANG等: "Reinforced Similarity Learning: Siamese Relation Networks for Robust Object Tracking", 《MM,20》 * |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113870330A (zh) * | 2021-09-30 | 2021-12-31 | 四川大学 | 基于特定标签和损失函数的孪生视觉跟踪方法 |
CN113870330B (zh) * | 2021-09-30 | 2023-05-12 | 四川大学 | 基于特定标签和损失函数的孪生视觉跟踪方法 |
CN113838099A (zh) * | 2021-10-20 | 2021-12-24 | 浙江大立科技股份有限公司 | 一种基于孪生神经网络的单目标跟踪方法 |
CN114299113A (zh) * | 2021-12-27 | 2022-04-08 | 北京航空航天大学 | 一种基于孪生网络的目标跟踪方法及装置 |
CN114529583A (zh) * | 2022-02-10 | 2022-05-24 | 国网河南省电力公司电力科学研究院 | 基于残差回归网络的电力设备跟踪方法及跟踪系统 |
CN114529583B (zh) * | 2022-02-10 | 2024-03-19 | 国网河南省电力公司电力科学研究院 | 基于残差回归网络的电力设备跟踪方法及跟踪系统 |
WO2023207742A1 (zh) * | 2022-04-28 | 2023-11-02 | 南京理工大学 | 一种交通异常行为检测方法与系统 |
CN114820712A (zh) * | 2022-05-16 | 2022-07-29 | 太原科技大学 | 一种自适应目标框优化的无人机跟踪方法 |
CN114820712B (zh) * | 2022-05-16 | 2024-04-02 | 太原科技大学 | 一种自适应目标框优化的无人机跟踪方法 |
CN115761393A (zh) * | 2022-10-18 | 2023-03-07 | 北京航空航天大学 | 一种基于模板在线学习的无锚目标跟踪方法 |
Also Published As
Publication number | Publication date |
---|---|
CN113255611B (zh) | 2021-10-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113255611B (zh) | 基于动态标签分配的孪生网络目标跟踪方法及移动设备 | |
CN108921873B (zh) | 基于核相关滤波优化的马尔科夫决策在线多目标跟踪方法 | |
CN107748873B (zh) | 一种融合背景信息的多峰目标跟踪方法 | |
CN109146912B (zh) | 一种基于目标性分析的视觉目标跟踪方法 | |
CN106981071B (zh) | 一种基于无人艇应用的目标跟踪方法 | |
CN110766723B (zh) | 一种基于颜色直方图相似性的无人机目标跟踪方法及系统 | |
CN112836639A (zh) | 基于改进YOLOv3模型的行人多目标跟踪视频识别方法 | |
CN107067410B (zh) | 一种基于增广样本的流形正则化相关滤波目标跟踪方法 | |
CN111582349B (zh) | 一种基于YOLOv3和核相关滤波改进的目标跟踪算法 | |
CN105809672A (zh) | 一种基于超像素和结构化约束的图像多目标协同分割方法 | |
CN110610165A (zh) | 一种基于yolo模型的船舶行为分析方法 | |
CN111523463B (zh) | 基于匹配-回归网络的目标跟踪方法及训练方法 | |
CN111091101A (zh) | 基于一步法的高精度行人检测方法、系统、装置 | |
CN111914832A (zh) | 一种rgb-d相机在动态场景下的slam方法 | |
CN113240716A (zh) | 一种多特征融合的孪生网络目标跟踪方法及系统 | |
Jiang et al. | High speed long-term visual object tracking algorithm for real robot systems | |
Zhang et al. | Structural pixel-wise target attention for robust object tracking | |
CN111914809B (zh) | 目标对象定位方法、图像处理方法、装置和计算机设备 | |
CN111768427B (zh) | 一种多运动目标跟踪方法、装置及存储介质 | |
CN113361329A (zh) | 一种基于实例特征感知的鲁棒单目标跟踪方法 | |
CN112907750A (zh) | 一种基于卷积神经网络的室内场景布局估计方法及系统 | |
CN116936116A (zh) | 一种智能医疗数据分析方法和系统 | |
CN112053384B (zh) | 基于边界框回归模型的目标跟踪方法 | |
CN113963021A (zh) | 一种基于时空特征和位置变化的单目标跟踪方法及系统 | |
CN116434049A (zh) | 参考角度确定方法、装置、设备及存储介质 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20210813 Assignee: Zhejiang Fengshou e-commerce Co.,Ltd. Assignor: ZHEJIANG NORMAL University Contract record no.: X2022980008844 Denomination of invention: Twin network target tracking method and mobile device based on dynamic label assignment Granted publication date: 20211001 License type: Common License Record date: 20220701 |
|
EE01 | Entry into force of recordation of patent licensing contract | ||
EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20210813 Assignee: Zhejiang yikangpu Medical Technology Co.,Ltd. Assignor: ZHEJIANG NORMAL University Contract record no.: X2023980045426 Denomination of invention: Twin Network Target Tracking Method and Mobile Device Based on Dynamic Label Allocation Granted publication date: 20211001 License type: Common License Record date: 20231101 |
|
EE01 | Entry into force of recordation of patent licensing contract | ||
EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20210813 Assignee: Xujing Chacang (Ningbo) Network Technology Co.,Ltd. Assignor: ZHEJIANG NORMAL University Contract record no.: X2024980000686 Denomination of invention: Twin Network Target Tracking Method Based on Dynamic Label Allocation and Mobile Devices Granted publication date: 20211001 License type: Common License Record date: 20240115 |
|
EE01 | Entry into force of recordation of patent licensing contract |