CN113065516B - 一种基于样本分离的无监督行人重识别***及方法 - Google Patents
一种基于样本分离的无监督行人重识别***及方法 Download PDFInfo
- Publication number
- CN113065516B CN113065516B CN202110436855.9A CN202110436855A CN113065516B CN 113065516 B CN113065516 B CN 113065516B CN 202110436855 A CN202110436855 A CN 202110436855A CN 113065516 B CN113065516 B CN 113065516B
- Authority
- CN
- China
- Prior art keywords
- pedestrian
- sample
- target domain
- loss function
- representing
- 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
- 238000000926 separation method Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000012549 training Methods 0.000 claims abstract description 38
- 238000005457 optimization Methods 0.000 claims abstract description 12
- 230000006870 function Effects 0.000 claims description 144
- 238000010606 normalization Methods 0.000 claims description 13
- 238000005259 measurement Methods 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000011524 similarity measure Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect 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
- G06V20/50—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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- 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
- 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
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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)
- Computer Vision & Pattern Recognition (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims (8)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110436855.9A CN113065516B (zh) | 2021-04-22 | 2021-04-22 | 一种基于样本分离的无监督行人重识别***及方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110436855.9A CN113065516B (zh) | 2021-04-22 | 2021-04-22 | 一种基于样本分离的无监督行人重识别***及方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113065516A CN113065516A (zh) | 2021-07-02 |
CN113065516B true CN113065516B (zh) | 2023-12-01 |
Family
ID=76567448
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110436855.9A Active CN113065516B (zh) | 2021-04-22 | 2021-04-22 | 一种基于样本分离的无监督行人重识别***及方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113065516B (zh) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113807401B (zh) * | 2021-08-18 | 2024-05-24 | 南京中兴力维软件有限公司 | 通用id识别方法、装置及设备 |
CN114140723B (zh) * | 2021-12-01 | 2023-07-04 | 北京有竹居网络技术有限公司 | 多媒体数据的识别方法、装置、可读介质及电子设备 |
CN114550221B (zh) * | 2022-04-22 | 2022-07-22 | 苏州浪潮智能科技有限公司 | 一种行人重识别方法、装置、设备及存储介质 |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108334849A (zh) * | 2018-01-31 | 2018-07-27 | 中山大学 | 一种基于黎曼流形的行人重识别方法 |
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 |
CN111144566A (zh) * | 2019-12-30 | 2020-05-12 | 深圳云天励飞技术有限公司 | 神经网络权重参数的训练方法、特征分类方法及对应装置 |
CN111259720A (zh) * | 2019-10-30 | 2020-06-09 | 北京中科研究院 | 基于自监督代理特征学习的无监督行人重识别方法 |
CN111738143A (zh) * | 2020-06-19 | 2020-10-02 | 重庆邮电大学 | 一种基于期望最大化的行人重识别方法 |
-
2021
- 2021-04-22 CN CN202110436855.9A patent/CN113065516B/zh active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108334849A (zh) * | 2018-01-31 | 2018-07-27 | 中山大学 | 一种基于黎曼流形的行人重识别方法 |
CN111259720A (zh) * | 2019-10-30 | 2020-06-09 | 北京中科研究院 | 基于自监督代理特征学习的无监督行人重识别方法 |
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 |
CN111144566A (zh) * | 2019-12-30 | 2020-05-12 | 深圳云天励飞技术有限公司 | 神经网络权重参数的训练方法、特征分类方法及对应装置 |
CN111738143A (zh) * | 2020-06-19 | 2020-10-02 | 重庆邮电大学 | 一种基于期望最大化的行人重识别方法 |
Non-Patent Citations (6)
Title |
---|
In Defense of the Triplet Loss for Person Re-Identification;Alexander Hermans 等;《https://arxiv.org/pdf/1703.07737.pdf》;1-17 * |
Person Re-Identification Using Hybrid Representation Reinforced by Metric Learning;Nazia Perwaiz 等;《IEEE Access》;第6卷;77334-77349 * |
Unsupervised Person Re-Identification Based on Measurement Axis;Jiahan Li 等;《IEEE SIGNAL PROCESSING LETTERS》;第28卷;379-383 * |
基于对抗生成网络的蒙特卡罗噪声去除算法;谢川 等;《模式识别与人工智能》;第31卷(第11期);1047-1060 * |
基于无监督域自适应的行人重识别算法研究;李佳函;《中国优秀硕士学位论文全文数据库 信息科技辑》(第2023(02)期);I138-1825 * |
面向智能监控的行人重识别方法研究;黎阳;《中国优秀硕士学位论文全文数据库 信息科技辑》(第2021(04)期);I138-858 * |
Also Published As
Publication number | Publication date |
---|---|
CN113065516A (zh) | 2021-07-02 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113065516B (zh) | 一种基于样本分离的无监督行人重识别***及方法 | |
CN111126360B (zh) | 基于无监督联合多损失模型的跨域行人重识别方法 | |
CN107885764B (zh) | 基于多任务深度学习快速哈希车辆检索方法 | |
CN107506740B (zh) | 一种基于三维卷积神经网络和迁移学习模型的人体行为识别方法 | |
CN107133569B (zh) | 基于泛化多标记学习的监控视频多粒度标注方法 | |
CN105574550A (zh) | 一种车辆识别方法及装置 | |
CN111652293B (zh) | 一种多任务联合判别学习的车辆重识别方法 | |
CN110837846A (zh) | 一种图像识别模型的构建方法、图像识别方法及装置 | |
EP3690741A2 (en) | Method for automatically evaluating labeling reliability of training images for use in deep learning network to analyze images, and reliability-evaluating device using the same | |
CN112633382B (zh) | 一种基于互近邻的少样本图像分类方法及*** | |
CN109299707A (zh) | 一种基于模糊深度聚类的无监督行人再识别方法 | |
CN106203490A (zh) | 一种安卓平台下基于属性学习和交互反馈的图像在线识别、检索方法 | |
CN105654066A (zh) | 一种车辆识别方法及装置 | |
CN111860106B (zh) | 一种无监督的桥梁裂缝识别方法 | |
CN111931505A (zh) | 一种基于子图嵌入的跨语言实体对齐方法 | |
US20230162522A1 (en) | Person re-identification method of integrating global features and ladder-shaped local features and device thereof | |
CN110598535A (zh) | 一种监控视频数据中使用的人脸识别分析方法 | |
CN104008395A (zh) | 一种基于人脸检索的不良视频智能检测方法 | |
CN109784288B (zh) | 一种基于判别感知融合的行人再识别方法 | |
JP2020119505A (ja) | マルチカメラシステム内のダブルエンベディング構成を利用して、道路利用者イベントを検出するために用いられるセグメンテーション性能向上のための学習方法及び学習装置、そしてこれを利用したテスティング方法及びテスティング装置。{learning method and learning device for improving segmentation performance to be used for detecting road user events using double embedding configuration in multi−camera system and testing method and testing device using the same} | |
CN108830236A (zh) | 一种基于深度特征的行人重识别方法 | |
CN114662497A (zh) | 一种基于协同神经网络的虚假新闻检测方法 | |
CN114842343A (zh) | 一种基于ViT的航空图像识别方法 | |
CN110443174A (zh) | 一种基于解耦自适应判别性特征学习的行人重识别方法 | |
CN113095229B (zh) | 一种无监督域自适应行人重识别***及方法 |
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: Cheng Deqiang Inventor after: Kou Qiqi Inventor after: Li Jiahan Inventor after: Li Yunlong Inventor after: Zhang Haoxiang Inventor after: Han Chenggong Inventor after: Xu Jinyang Inventor after: Zhang Yunhe Inventor after: Li Chao Inventor before: Li Jiahan Inventor before: Li Yunlong Inventor before: Cheng Deqiang Inventor before: Kou Qiqi Inventor before: Zhang Haoxiang Inventor before: Han Chenggong Inventor before: Xu Jinyang Inventor before: Zhang Yunhe Inventor before: Li Chao |
|
GR01 | Patent grant | ||
GR01 | Patent grant |