[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]
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基于AR-SSVEP和YOLOv3的时敏目标识别方法()
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《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:
2024-11-30

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.

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更新日期/Last Update: 2024-10-10