[1]马留洋,胡争争,栗武华.基于AR-SSVEP和YOLOv3的时敏目标识别方法[J].郑州大学学报(工学版),2024,45(pre):2.[doi:10.13705/j.issn.1671-6833.2025.01.017]
　MA Liuyang,HU Zhengzheng,LI Wuhua.HE L. Key Target Person Detection and Tracking Based on Fragmented Video Information[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023[J].Journal of Zhengzhou University (Engineering Science),2024,45(pre):2.[doi:10.13705/j.issn.1671-6833.2025.01.017]
点击复制

基于AR-SSVEP和YOLOv3的时敏目标识别方法()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年pre
页码:
2
栏目:
出版日期:
2026-01-10

文章信息/Info

Title:
HE L. Key Target Person Detection and Tracking Based on Fragmented Video Information[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023
作者:
马留洋1; 胡争争1; 栗武华1
(1.中国电子科技集团公司第二十七研究所,河南 郑州 450047)
Author(s):
MA Liuyang1;  HU Zhengzheng1;  LI Wuhua1
(1. The 27th Research Institute of China Electronics Technology Group Corporation, Zhengzhou, 450047, China)
关键词:
增强现实; 人工智能; 时敏目标; 目标检测; 稳态视觉诱发电位; 目标识别
Keywords:
Augmented reality;  Artificial intelligence;  Time sensitive targets;  Object detection;  Steady state visual evoked potential;  Target recognition
分类号:
R318. 04TP391. 4
DOI:
10.13705/j.issn.1671-6833.2025.01.017
文献标志码:
A
摘要:
针对目标跟踪过程中目标身份（identification, ID）跳变而影响时敏目标识别的问题，本研究提出了一种融合增强现实技术（Augmented Reality, AR）、稳态视觉诱发电位（Steady-State Visual Evoked Potentials, SSVEP）和YOLOv3的人在回路的“检测-决策”时敏目标识别方法（AR-SSVEP-YOLOV3）。利用目标感知模块获取前端场景视频，并通过增强现实眼镜实时呈现，YOLOv3算法完成场景视频中敏感目标检测，AR-SSVEP脑电处理模块解析受试者的脑电数据，在ID变化过程中对时敏目标进行识别，对比分析时敏目标的识别正确率，AR-SSVEP-YOLOV3时敏目标识别方法相比YOLOv3算法识别正确率平均提升了40%左右，相比YOLOv3-Sort算法平均提升了15%左右。实验结果表明：AR-SSVEP-YOLOV3时敏目标识别方法可以降低目标ID跳变对时敏目标识别的影响，提升人机交互能力和时敏目标识别正确率。
Abstract:
To address the problem of target identity (identification, ID) fluctuation during target tracking, affecting the time-sensitive target recognition, this study proposed an "detection-decision" time-sensitive target recognition method (AR-SSVEP-YOLOV3) that integrates augmented reality (AR) technology, steady-state visual evoked potentials (SSVEP), and YOLOv3. The target perception module obtains the front-end scene video and presents it in real-time through an AR headset. The YOLOv3 algorithm completes the detection of sensitive targets in the scene video, and the AR-SSVEP EEG processing module decodes the EEG data of the subject during ID changes to identify time-sensitive targets. The correct recognition rate of time-sensitive targets is compared and analyzed, and the average improvement in the recognition rate of AR-SSVEP-YOLOV3 time-sensitive target recognition method compared to the YOLOv3 algorithm is about 40%, and the average improvement compared to the YOLOv3-Sort algorithm is about 15%. The experimental results show that the AR-SSVEP-YOLOV3 time-sensitive target recognition method can reduce the influence of target ID fluctuation on time-sensitive target recognition and improve the human-computer interaction ability and the correct recognition rate of time-sensitive targets.

参考文献/References:

[1] 吴晗, 张志龙, 李楚为, 等. 时敏目标的类型与瞄准点识别算法[J]. 航空兵器, 2022,29(2):24-29.

WU H, ZHANG Z L , Li C W , et al . Recognition Algorithm for Types and Aiming Point of the Time －Sensitive Targets[J]. Aero Weaponry , 2022 ,29(2):24－29.

[2] GURKAN B. Robust object tracking via integration of particle filtering with deep detection[J]. Digital Signal Processing, 2019,87:112-124.

[3] CHEN J, LIN Y, HUANG D, et al. Robust tracking algorithm for infrared target via correlation filter and particle filter - ScienceDirect[J]. Infrared Physics & Technology, 2020,111: 103516-.

[4] 周孟然, 李学松, 朱梓伟, 等. 井下矿工多目标检测与跟踪联合算法[J]. 工矿自动化, 2022,48(10):40-47.

ZHOU M R,LI X S, ZHU Z W, et al. A joint algorithm of multi-target detection and tracking for underground miners[J]. Journal of Mine Automation, 2022,48(10):40-47.

[5] 黄培荣, 宋剑, 李楠. 打击时敏目标的决策任务层次分解[J]. 计算机与数字工程, 2014,42(10):1905-1907.

HUANG P R, SONG J,LI N. Decision-making task hierarchical decomposition of blowing time sensitive target[J]. Computer & Digital Engineering, 2014, 42(10):1905-0907.

[6] ARPAIA P, DURACCIO L, MOCCALDI N, et al. Wearable brain–computer interface instrumentation for robot-based rehabilitation by augmented reality[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(9): 6362–6371.

[7] 黄章瑞. 情境驱动的AR--BCI脑机交互方法研究[D]. 郑州大学计算机技术, 2021.

HUANG Z R. Research on Situations drive Brain-·Computer Interaction based on AR-BCI [D]. Zhengzhou: Zhengzhou University , 2021.

[8] 蒋永玉. 假手脑控的增强现实视觉刺激范式及其异步应用系统研究[D]. 西安交通大学, 2022.

JIANG Y Y. Research on Augmented-Reality Visually Stimulus Paradigm and Its Asynchronous BCI System for Prosthetic Hands [D], Xi’an: Xi’an Jiaotong University, 2022

[9] FANG B, DING W, SUN F, et al. Brain-Computer Interface Integrated With Augmented Reality for Human-Robot Interaction[J]. IEEE transactions on cognitive and developmental systems, 2022,15(4):1702-1711.

[10] ZHANG R, CAO L, XU Z, et al. Improving AR-SSVEP Recognition Accuracy Under High Ambient Brightness Through Iterative Learning[J]. IEEE Trans Neural Syst Rehabil Eng, 2023,31:1796-1806.

[11] 邹霞. 基于SSVEP的脑机接口系统设计与研究[D]. 南昌大学通信与信息系统, 2014.

ZOU X. Design and research of brain-computer interface system based on SSVEP[D], Nanchang: Nanchang University, 2014.

[12] 白雪鹏. 基于改进YOLO V5算法的遮挡行人检测应用研究[D]. 大连交通大学, 2023.

BAI X P. Improved YOLO V5 Algorithm based on Obscured Pedestrian Detection Application Research[D]. Dalian: Dalian Jiaotong University, 2023

[13] 院老虎, 常玉坤, 刘家夫. 基于改进YOLOv5s的雾天场景车辆检测方法[J]. 郑州大学学报（工学版）, 2023,44(3):35-41.

YUAN L H, CHANG Y K, LIU J F. Vehicle detection method based on improved YOLOv5s in foggy scene[J]. Journal of Zhengzhou University (Engineering Science), 2023,44(3):35-41.

[14] 李北明, 金荣璐, 徐召飞, 等. 基于特征蒸馏的改进Ghost-YOLOv5红外目标检测算法[J]. 郑州大学学报:工学版, 2022,43(1):7.

LI B M, JIN R L, XU Z F, et al. An improved Ghost-YOLOv5 infrared target detection algorithm based on feature distillation[J]. Journal of Zhengzhou University (Engineering Science), 2022,43(01):20-26.

[15] HORII S, N AKAUCHI S . AR-SSVEP for brain-machine interface: Estimating user’s gaze in head-mounted display with USB camera: 2015 IEEE Virtual Reality (VR)[C], 2015.

[16] HAKIM S ,JIMMY P ,CAMILLE J, et al. Towards BCI-based Interfaces for Augmented Reality: Feasibility, Design and Evaluation[J]. IEEE Transactions on Visualization and Computer Graphics, 2020,26(3):1608-1621.

[17] SHAO X, L IN M. Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification[J]. Cognitive Neurodynamics, 2020,14(6).

[18] YIN X, L IN M. Multi-information improves the performance of CCA-based SSVEP classification[J]. Cogn Neurodyn, 2024,18(1):165-172.

[19] ZHAO X, LIU C, XU Z, et al. SSVEP Stimulus Layout Effect on Accuracy of Brain-Computer Interfaces in Augmented Reality Glasses[J]. IEEE access, 2020,8:5990-5998.

[20] WANG K, ZHAI D H, XIONG Y, et al. An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control[J]. IEEE transactions on neural networks and learning systems, 2022(5):33.

[21] 张力新,张裕坤,柯余峰,等.基于Hololens的增强现实脑-机接口研究[J].中国生物医学工程学报,2019,38(1):51-58.

ZHANG L X, ZHANG Y K, KE Y F, et al. A study of argument reality based brain-computer interface(AR-BCI) in Hololens[J]. Chinese Journal of Biomedical Engineering, 2019,38(1):51-58

[22] 何丽. 基于碎片化视频信息的关键目标人物检测和跟踪[D]. 南京邮电大学, 2023.

相似文献/References:

[1]马民,秦佳,杨东升,等.人工智能在电力系统中的应用综述[J].郑州大学学报(工学版),2019,40(05):22.[doi:10.13705/j.issn.1671-6833.2019.05.012]
　Ma Min,Qin Jia,Yang Dongsheng,et al.A review of the application of artificial intelligence in power system[J].Journal of Zhengzhou University (Engineering Science),2019,40(pre):22.[doi:10.13705/j.issn.1671-6833.2019.05.012]
[2]李春雨,谭同德,翟震..原型设计法在智能CAD中的应用研究[J].郑州大学学报(工学版),2001,22(03):81.[doi:10.3969/j.issn.1671-6833.2001.03.025]
　LI Chunyu,Tongde Tan,Zhai Zhen.Research on the application of prototyping method in intelligent CAD[J].Journal of Zhengzhou University (Engineering Science),2001,22(pre):81.[doi:10.3969/j.issn.1671-6833.2001.03.025]
[3]高学军,张琳娜,苏智剑,等.基于人工智能的零件配合设计方法研究[J].郑州大学学报(工学版),1998,19(04):26.
　Gao Xuejun,Zhang Linna,Su Zhijian,et al.Research on the design method based on artificial intelligence -based parts coordination[J].Journal of Zhengzhou University (Engineering Science),1998,19(pre):26.
[4]韩兵.基于知识分层的智能校正控制[J].郑州大学学报(工学版),1990,11(03):102.
　Han Bing,Intelligent correction control based on knowledge[J].Journal of Zhengzhou University (Engineering Science),1990,11(pre):102.
[5]王国中.开发事务处理专家系统——快速原型化方法[J].郑州大学学报(工学版),1990,11(03):130.
　Wang Guozhong,Development transaction processing expert system -fast prototype method[J].Journal of Zhengzhou University (Engineering Science),1990,11(pre):130.

更新日期/Last Update: 2024-10-10