NL2023430B1 - Target Tracking Method Based on Improved Particle Swarm Optimization Algorithm - Google Patents

Target Tracking Method Based on Improved Particle Swarm Optimization Algorithm Download PDF

Info

Publication number
NL2023430B1
NL2023430B1 NL2023430A NL2023430A NL2023430B1 NL 2023430 B1 NL2023430 B1 NL 2023430B1 NL 2023430 A NL2023430 A NL 2023430A NL 2023430 A NL2023430 A NL 2023430A NL 2023430 B1 NL2023430 B1 NL 2023430B1
Authority
NL
Netherlands
Prior art keywords
target
optimization algorithm
particle swarm
swarm optimization
particle
Prior art date
Application number
NL2023430A
Other languages
English (en)
Dutch (nl)
Inventor
Sun Hui
Li Jiabin
Deng Rui
Li Meng
Xia Longlong
Zou Shigui
Liao Xiaolong
Wang Xuyu
Original Assignee
Chengdu Qitai Zhilian Information Tech Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Chengdu Qitai Zhilian Information Tech Co Ltd filed Critical Chengdu Qitai Zhilian Information Tech Co Ltd
Application granted granted Critical
Publication of NL2023430B1 publication Critical patent/NL2023430B1/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/277—Analysis of motion involving stochastic approaches, e.g. using Kalman filters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • 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
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • 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
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • 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
    • G06V10/62—Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07—Target detection

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Astronomy & Astrophysics (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Remote Sensing (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Image Analysis (AREA)
NL2023430A 2019-06-05 2019-07-03 Target Tracking Method Based on Improved Particle Swarm Optimization Algorithm NL2023430B1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910484131.4A CN110288634A (zh) 2019-06-05 2019-06-05 一种基于改进粒子群优化算法的目标跟踪方法

Publications (1)

Publication Number Publication Date
NL2023430B1 true NL2023430B1 (en) 2020-10-06

Family

ID=67383684

Family Applications (1)

Application Number Title Priority Date Filing Date
NL2023430A NL2023430B1 (en) 2019-06-05 2019-07-03 Target Tracking Method Based on Improved Particle Swarm Optimization Algorithm

Country Status (4)

Country Link
CN (1) CN110288634A (zh)
BE (1) BE1027208B1 (zh)
LU (1) LU101298B1 (zh)
NL (1) NL2023430B1 (zh)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111242971B (zh) * 2019-12-03 2023-05-02 西安电子科技大学 一种基于改进的双中心粒子群优化算法的目标跟踪方法
CN111666860A (zh) * 2020-06-01 2020-09-15 浙江省机电设计研究院有限公司 一种车牌信息与车辆特征融合的车辆轨迹跟踪方法
CN117152258B (zh) * 2023-11-01 2024-01-30 中国电建集团山东电力管道工程有限公司 一种管道生产智慧车间的产品定位方法及系统

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103914124A (zh) * 2014-04-04 2014-07-09 浙江工商大学 面向三维场景的节能颜色映射方法

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103901887B (zh) * 2014-03-04 2017-05-24 重庆邮电大学 一种基于改进粒子群算法的多移动机器人编队控制方法
US10013513B2 (en) * 2014-10-31 2018-07-03 International Business Machines Corporation Accelerating particle-swarm algorithms
CN104820997B (zh) * 2015-05-14 2016-12-21 北京理工大学 一种基于分块稀疏表达与hsv特征融合的目标跟踪方法
CN104915969B (zh) * 2015-05-21 2019-01-18 云南大学 一种基于粒子群优化的模版匹配跟踪方法
CN106447027A (zh) * 2016-10-13 2017-02-22 河海大学 一种矢量高斯学习的粒子群优化方法
CN106682682A (zh) * 2016-10-20 2017-05-17 北京工业大学 一种基于粒子群优化算法对支持向量机的优化方法
CN106709958A (zh) * 2016-12-03 2017-05-24 浙江大学 一种基于灰度梯度和颜色直方图的图像质量评价方法
CN106650808A (zh) * 2016-12-20 2017-05-10 北京工业大学 一种基于量子近邻算法的图像分类方法
CN107150341A (zh) * 2017-06-13 2017-09-12 南京理工大学 一种基于离散粒子群算法的焊接机器人焊接路径规划方法
CN108471143A (zh) * 2018-03-26 2018-08-31 国网天津市电力公司电力科学研究院 基于正负反馈粒子群算法的微电网多能源调度优化方法
CN108564631B (zh) * 2018-04-03 2021-07-09 上海理工大学 车灯光导色差检测方法、装置及计算机可读存储介质
CN109146922B (zh) * 2018-07-11 2021-07-06 哈尔滨工程大学 一种基于自适应粒子群优化的前视声纳水下目标跟踪方法
CN109544562B (zh) * 2018-11-09 2022-03-22 北京工业大学 基于图像的钢筋端面自动识别计数算法

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103914124A (zh) * 2014-04-04 2014-07-09 浙江工商大学 面向三维场景的节能颜色映射方法

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
BAE CHANGSEOK ET AL: "A novel real time video tracking framework using adaptive discrete swarm optimization", EXPERT SYSTEMS WITH APPLICATIONS, OXFORD, GB, vol. 64, 4 August 2016 (2016-08-04), pages 385 - 399, XP029712698, ISSN: 0957-4174, DOI: 10.1016/J.ESWA.2016.08.027 *
BAE CTEUNG H W FCHUNG Y Y: "Effective object tracking framework using weight adjustment of particle swarm optimization", INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING. IEEE COMPUTER SOCIETY, 2018, pages 831 - 833, XP033334636, doi:10.1109/ICOIN.2018.8343236
FENG SHA ET AL: "A categorized Particle Swarm Optimization for object tracking", 2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 25 May 2015 (2015-05-25), pages 2737 - 2744, XP033203623, DOI: 10.1109/CEC.2015.7257228 *
GUO SIQIUXU TINGFAWANG HONGQING ET AL.: "An improved particle swarm optimization target tracking method", CHINA OPTICS, vol. 7, no. 5, 2014, pages 759 - 767
LIU GUANG ET AL: "A New Weight Adjusted Particle Swarm Optimization for Real-Time Multiple Object Tracking", 30 September 2016, INTERNATIONAL CONFERENCE ON FINANCIAL CRYPTOGRAPHY AND DATA SECURITY; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE(S) 643 - 651, ISBN: 978-3-642-17318-9, XP047410032 *
NOUIRI MBEKRAR AJEMAI A ET AL.: "An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem", JOURNAL OF INTELLIGENT MANUFACTURING, 2018, pages 1 - 13
SHI Y ET AL: "A modified particle swarm optimizer", EVOLUTIONARY COMPUTATION PROCEEDINGS, 1998. IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE., THE 1998 IEEE INTERNATIONAL CONFERENCE ON, IEEE, NEW YORK, NY, USA, 4 May 1998 (1998-05-04), pages 69 - 73, XP010288771, ISBN: 978-0-7803-4869-1, DOI: 10.1109/ICEC.1998.699146 *
YIN HONGPENGLIU ZHAODONGLUO XIANKE ET AL.: "A target tracking feature selection algorithm based on the particle swarm optimization", COMPUTER ENGINEERING AND APPLICATIONS, vol. 49, no. 17, 2013, pages 164 - 168

Also Published As

Publication number Publication date
BE1027208A1 (fr) 2020-11-17
BE1027208B1 (fr) 2020-11-24
LU101298B1 (en) 2020-11-10
CN110288634A (zh) 2019-09-27

Similar Documents

Publication Publication Date Title
NL2023430B1 (en) Target Tracking Method Based on Improved Particle Swarm Optimization Algorithm
Liu et al. Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring
Kuznichov et al. Data augmentation for leaf segmentation and counting tasks in rosette plants
CN103886107B (zh) 基于天花板图像信息的机器人定位与地图构建系统
CN102141838B (zh) 基于视觉的身份跟踪
CN107610175A (zh) 基于半直接法和滑动窗口优化的单目视觉slam算法
CN112037269B (zh) 一种基于多域协同特征表达的视觉运动目标跟踪方法
Fischer et al. Qdtrack: Quasi-dense similarity learning for appearance-only multiple object tracking
CN112686928B (zh) 一种基于多源信息融合的运动目标视觉跟踪方法
Houben et al. Efficient multi-camera visual-inertial SLAM for micro aerial vehicles
EP3161725B1 (en) Color identification using infrared imaging
García et al. Adaptive multi-cue 3D tracking of arbitrary objects
WO2024114119A1 (zh) 一种基于双目相机引导的传感器融合方法
Chen et al. Mp6d: An rgb-d dataset for metal parts’ 6d pose estimation
CN109318227A (zh) 一种基于人形机器人的掷骰子方法及人形机器人
Liu et al. Robotic picking in dense clutter via domain invariant learning from synthetic dense cluttered rendering
Zheng et al. A study of 3D feature tracking and localization using a stereo vision system
CN111476869A (zh) 一种面向计算媒体的虚拟摄像机规划方法
Huang et al. MC-VEO: A visual-event odometry with accurate 6-DoF motion compensation
Coluccia et al. Drone-vs-bird detection challenge at ICIAP 2021
Xiao et al. Research of the fish tracking method with occlusion based on monocular stereo vision
Blum et al. Self-improving semantic perception for indoor localisation
Sun et al. Automatic targetless calibration for LiDAR and camera based on instance segmentation
Zhu et al. Event camera-based visual odometry for dynamic motion tracking of a legged robot using adaptive time surface
Li et al. Unsupervised joint learning of depth, optical flow, ego-motion from video

Legal Events

Date Code Title Description
MM Lapsed because of non-payment of the annual fee

Effective date: 20220801