CN111598004B - 一种渐进增强自学习的无监督跨领域行人再识别方法 - Google Patents
一种渐进增强自学习的无监督跨领域行人再识别方法 Download PDFInfo
- Publication number
- CN111598004B CN111598004B CN202010420131.0A CN202010420131A CN111598004B CN 111598004 B CN111598004 B CN 111598004B CN 202010420131 A CN202010420131 A CN 202010420131A CN 111598004 B CN111598004 B CN 111598004B
- Authority
- CN
- China
- Prior art keywords
- network model
- target domain
- pedestrian
- similarity
- training
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000000750 progressive effect Effects 0.000 title claims abstract description 14
- 230000002787 reinforcement Effects 0.000 title claims abstract description 9
- 238000012549 training Methods 0.000 claims abstract description 66
- 238000012512 characterization method Methods 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims abstract description 18
- 238000012360 testing method Methods 0.000 claims description 15
- 238000011176 pooling Methods 0.000 claims description 14
- 238000005065 mining Methods 0.000 claims description 5
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000012986 modification Methods 0.000 claims description 2
- 230000004048 modification Effects 0.000 claims description 2
- 230000006870 function Effects 0.000 abstract description 27
- 239000013598 vector Substances 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011423 initialization method Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/30—Scenes; Scene-specific elements in albums, collections or shared content, e.g. social network photos or video
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/29—Graphical models, e.g. Bayesian networks
-
- 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
- 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
- G06N3/047—Probabilistic or stochastic networks
-
- 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
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Multimedia (AREA)
- Probability & Statistics with Applications (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims (1)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010420131.0A CN111598004B (zh) | 2020-05-18 | 2020-05-18 | 一种渐进增强自学习的无监督跨领域行人再识别方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010420131.0A CN111598004B (zh) | 2020-05-18 | 2020-05-18 | 一种渐进增强自学习的无监督跨领域行人再识别方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111598004A CN111598004A (zh) | 2020-08-28 |
CN111598004B true CN111598004B (zh) | 2023-12-08 |
Family
ID=72185679
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010420131.0A Active CN111598004B (zh) | 2020-05-18 | 2020-05-18 | 一种渐进增强自学习的无监督跨领域行人再识别方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111598004B (zh) |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112418289B (zh) * | 2020-11-17 | 2021-08-03 | 北京京航计算通讯研究所 | 一种不完全标注数据的多标签分类处理方法及装置 |
CN112508130A (zh) * | 2020-12-25 | 2021-03-16 | 商汤集团有限公司 | 聚类方法及装置、电子设备和存储介质 |
CN113011568B (zh) * | 2021-03-31 | 2024-06-21 | 华为技术有限公司 | 一种模型的训练方法、数据处理方法及设备 |
CN113158955B (zh) * | 2021-04-30 | 2024-02-20 | 杭州电子科技大学 | 基于聚类引导和成对度量三元组损失的行人重识别方法 |
CN113326826A (zh) * | 2021-08-03 | 2021-08-31 | 新石器慧通(北京)科技有限公司 | 网络模型的训练方法、装置、电子设备及存储介质 |
CN114549473B (zh) * | 2022-02-23 | 2024-04-19 | 中国民用航空总局第二研究所 | 具备自主学习快速适应能力的道面检测方法及*** |
Citations (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108388574A (zh) * | 2018-01-11 | 2018-08-10 | 同济大学 | 基于triplet深度二值网络的快速人脸检索方法 |
CN108764281A (zh) * | 2018-04-18 | 2018-11-06 | 华南理工大学 | 一种基于半监督自步学习跨任务深度网络的图像分类方法 |
CN108921107A (zh) * | 2018-07-06 | 2018-11-30 | 北京市新技术应用研究所 | 基于排序损失和Siamese网络的行人再识别方法 |
KR20180133657A (ko) * | 2017-06-07 | 2018-12-17 | 한화에어로스페이스 주식회사 | 기계 학습을 이용한 다중 뷰포인트 차량 인식 장치 |
CN109086672A (zh) * | 2018-07-05 | 2018-12-25 | 襄阳矩子智能科技有限公司 | 一种基于强化学习自适应分块的行人再识别方法 |
CN109299707A (zh) * | 2018-10-30 | 2019-02-01 | 天津师范大学 | 一种基于模糊深度聚类的无监督行人再识别方法 |
CN109635695A (zh) * | 2018-11-28 | 2019-04-16 | 西安理工大学 | 基于三元组卷积神经网络的行人再识别方法 |
CN110135295A (zh) * | 2019-04-29 | 2019-08-16 | 华南理工大学 | 一种基于迁移学习的无监督行人重识别方法 |
CN110189315A (zh) * | 2019-05-28 | 2019-08-30 | 李峥嵘 | 一种风电机组叶片无人机自动定位方法 |
CN110263697A (zh) * | 2019-06-17 | 2019-09-20 | 哈尔滨工业大学(深圳) | 基于无监督学习的行人重识别方法、装置及介质 |
CN110321813A (zh) * | 2019-06-18 | 2019-10-11 | 南京信息工程大学 | 基于行人分割的跨域行人重识别方法 |
CN110414462A (zh) * | 2019-08-02 | 2019-11-05 | 中科人工智能创新技术研究院(青岛)有限公司 | 一种无监督的跨域行人重识别方法及*** |
CN110580460A (zh) * | 2019-08-28 | 2019-12-17 | 西北工业大学 | 基于行人身份和属性特征联合识别验证的行人再识别方法 |
CN110674292A (zh) * | 2019-08-27 | 2020-01-10 | 腾讯科技(深圳)有限公司 | 一种人机交互方法、装置、设备及介质 |
CN110728263A (zh) * | 2019-10-24 | 2020-01-24 | 中国石油大学(华东) | 一种基于距离选择的强判别特征学习的行人重识别方法 |
CN110728216A (zh) * | 2019-09-27 | 2020-01-24 | 西北工业大学 | 一种基于行人属性自适应学习的无监督行人再识别方法 |
CN110796057A (zh) * | 2019-10-22 | 2020-02-14 | 上海交通大学 | 行人重识别方法、装置及计算机设备 |
CN110942025A (zh) * | 2019-11-26 | 2020-03-31 | 河海大学 | 一种基于聚类的无监督跨域行人重识别方法 |
CN111027442A (zh) * | 2019-12-03 | 2020-04-17 | 腾讯科技(深圳)有限公司 | 用于行人重识别的模型训练方法、识别方法、装置及介质 |
CN111126360A (zh) * | 2019-11-15 | 2020-05-08 | 西安电子科技大学 | 基于无监督联合多损失模型的跨域行人重识别方法 |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018081135A1 (en) * | 2016-10-25 | 2018-05-03 | Vmaxx Inc. | Point to set similarity comparison and deep feature learning for visual recognition |
US11205103B2 (en) * | 2016-12-09 | 2021-12-21 | The Research Foundation for the State University | Semisupervised autoencoder for sentiment analysis |
US20190197398A1 (en) * | 2017-12-27 | 2019-06-27 | Microsoft Technology Licensing, Llc | Embedded learning for response prediction |
US10373056B1 (en) * | 2018-01-25 | 2019-08-06 | SparkCognition, Inc. | Unsupervised model building for clustering and anomaly detection |
US11537817B2 (en) * | 2018-10-18 | 2022-12-27 | Deepnorth Inc. | Semi-supervised person re-identification using multi-view clustering |
WO2020093303A1 (zh) * | 2018-11-08 | 2020-05-14 | 北京比特大陆科技有限公司 | 基于人脸识别的处理方法、装置、设备及可读存储介质 |
GB2586265B (en) * | 2019-08-15 | 2023-02-15 | Vision Semantics Ltd | Text based image search |
-
2020
- 2020-05-18 CN CN202010420131.0A patent/CN111598004B/zh active Active
Patent Citations (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20180133657A (ko) * | 2017-06-07 | 2018-12-17 | 한화에어로스페이스 주식회사 | 기계 학습을 이용한 다중 뷰포인트 차량 인식 장치 |
CN108388574A (zh) * | 2018-01-11 | 2018-08-10 | 同济大学 | 基于triplet深度二值网络的快速人脸检索方法 |
CN108764281A (zh) * | 2018-04-18 | 2018-11-06 | 华南理工大学 | 一种基于半监督自步学习跨任务深度网络的图像分类方法 |
CN109086672A (zh) * | 2018-07-05 | 2018-12-25 | 襄阳矩子智能科技有限公司 | 一种基于强化学习自适应分块的行人再识别方法 |
CN108921107A (zh) * | 2018-07-06 | 2018-11-30 | 北京市新技术应用研究所 | 基于排序损失和Siamese网络的行人再识别方法 |
CN109299707A (zh) * | 2018-10-30 | 2019-02-01 | 天津师范大学 | 一种基于模糊深度聚类的无监督行人再识别方法 |
CN109635695A (zh) * | 2018-11-28 | 2019-04-16 | 西安理工大学 | 基于三元组卷积神经网络的行人再识别方法 |
CN110135295A (zh) * | 2019-04-29 | 2019-08-16 | 华南理工大学 | 一种基于迁移学习的无监督行人重识别方法 |
CN110189315A (zh) * | 2019-05-28 | 2019-08-30 | 李峥嵘 | 一种风电机组叶片无人机自动定位方法 |
CN110263697A (zh) * | 2019-06-17 | 2019-09-20 | 哈尔滨工业大学(深圳) | 基于无监督学习的行人重识别方法、装置及介质 |
CN110321813A (zh) * | 2019-06-18 | 2019-10-11 | 南京信息工程大学 | 基于行人分割的跨域行人重识别方法 |
CN110414462A (zh) * | 2019-08-02 | 2019-11-05 | 中科人工智能创新技术研究院(青岛)有限公司 | 一种无监督的跨域行人重识别方法及*** |
CN110674292A (zh) * | 2019-08-27 | 2020-01-10 | 腾讯科技(深圳)有限公司 | 一种人机交互方法、装置、设备及介质 |
CN110580460A (zh) * | 2019-08-28 | 2019-12-17 | 西北工业大学 | 基于行人身份和属性特征联合识别验证的行人再识别方法 |
CN110728216A (zh) * | 2019-09-27 | 2020-01-24 | 西北工业大学 | 一种基于行人属性自适应学习的无监督行人再识别方法 |
CN110796057A (zh) * | 2019-10-22 | 2020-02-14 | 上海交通大学 | 行人重识别方法、装置及计算机设备 |
CN110728263A (zh) * | 2019-10-24 | 2020-01-24 | 中国石油大学(华东) | 一种基于距离选择的强判别特征学习的行人重识别方法 |
CN111126360A (zh) * | 2019-11-15 | 2020-05-08 | 西安电子科技大学 | 基于无监督联合多损失模型的跨域行人重识别方法 |
CN110942025A (zh) * | 2019-11-26 | 2020-03-31 | 河海大学 | 一种基于聚类的无监督跨域行人重识别方法 |
CN111027442A (zh) * | 2019-12-03 | 2020-04-17 | 腾讯科技(深圳)有限公司 | 用于行人重识别的模型训练方法、识别方法、装置及介质 |
Non-Patent Citations (1)
Title |
---|
Xinyu zhang.Self-TrainingWith Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification.《IEEE》.2020,8221-8230. * |
Also Published As
Publication number | Publication date |
---|---|
CN111598004A (zh) | 2020-08-28 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111598004B (zh) | 一种渐进增强自学习的无监督跨领域行人再识别方法 | |
CN109949317B (zh) | 基于逐步对抗学习的半监督图像实例分割方法 | |
CN110414462B (zh) | 一种无监督的跨域行人重识别方法及*** | |
CN113378632B (zh) | 一种基于伪标签优化的无监督域适应行人重识别方法 | |
CN112101150B (zh) | 一种基于朝向约束的多特征融合行人重识别方法 | |
CN111814584B (zh) | 基于多中心度量损失的多视角环境下车辆重识别方法 | |
CN107679078B (zh) | 一种基于深度学习的卡口图像车辆快速检索方法及*** | |
US10102443B1 (en) | Hierarchical conditional random field model for labeling and segmenting images | |
Liu et al. | Nonparametric scene parsing via label transfer | |
CN110942025A (zh) | 一种基于聚类的无监督跨域行人重识别方法 | |
Endres et al. | Category-independent object proposals with diverse ranking | |
Zhang et al. | Weakly supervised photo cropping | |
CN109063649B (zh) | 基于孪生行人对齐残差网络的行人重识别方法 | |
CN110321967B (zh) | 基于卷积神经网络的图像分类改进方法 | |
CN110647907B (zh) | 利用多层分类和字典学习的多标签图像分类算法 | |
US20210319215A1 (en) | Method and system for person re-identification | |
CN109635708B (zh) | 一种基于三数据集交叉迁移学习的无监督行人再识别方法 | |
CN104281572B (zh) | 一种基于互信息的目标匹配方法及其*** | |
CN110443257B (zh) | 一种基于主动学习的显著性检测方法 | |
Niu et al. | A novel image retrieval method based on multi-features fusion | |
CN112633382A (zh) | 一种基于互近邻的少样本图像分类方法及*** | |
CN111582178A (zh) | 基于多方位信息和多分支神经网络车辆重识别方法及*** | |
CN113065409A (zh) | 一种基于摄像分头布差异对齐约束的无监督行人重识别方法 | |
Kim et al. | A shape preserving approach for salient object detection using convolutional neural networks | |
CN117152459A (zh) | 图像检测方法、装置、计算机可读介质及电子设备 |
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 | ||
CB03 | Change of inventor or designer information | ||
CB03 | Change of inventor or designer information |
Inventor after: Li Zhengrong Inventor after: Zhang Xinyu Inventor before: Shen Chunhua Inventor before: Zhang Xinyu Inventor before: Li Zhengrong |
|
TA01 | Transfer of patent application right | ||
TA01 | Transfer of patent application right |
Effective date of registration: 20200930 Address after: 308-8, 3 / F, building 1, yard 1, energy East Road, Shahe Town, Changping District, Beijing Applicant after: BEIJING XINGGUANG SHITU TECHNOLOGY Co.,Ltd. Applicant after: Li Zhengrong Address before: 308-8, 3 / F, building 1, yard 1, energy East Road, Shahe Town, Changping District, Beijing Applicant before: BEIJING XINGGUANG SHITU TECHNOLOGY Co.,Ltd. Applicant before: Li Zhengrong Applicant before: Shen Chunhua |
|
CB02 | Change of applicant information | ||
CB02 | Change of applicant information |
Address after: Room 2904, No. 41 Coastal City, Wuxi Economic Development Zone, Jiangsu Province, 214000 Applicant after: Jiangsu Xingshan Shitu Technology (Group) Co.,Ltd. Applicant after: Li Zhengrong Address before: 102206 308-8, 3 / F, building 1, yard 1, Nengyuan East Road, Shahe Town, Changping District, Beijing Applicant before: BEIJING XINGGUANG SHITU TECHNOLOGY CO.,LTD. Applicant before: Li Zhengrong |
|
GR01 | Patent grant | ||
GR01 | Patent grant |