HE L. Key Target Person Detection and Tracking Based on Fragmented Video Information[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023

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HE L. Key Target Person Detection and Tracking Based on Fragmented Video Information[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 pre Page number: 2- Column: Public date: 2024-12-31

Title:: HE L. Key Target Person Detection and Tracking Based on Fragmented Video Information[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023

Author(s):: MA Liuyang1; HU Zhengzheng1; LI Wuhua1; (1. The 27th Research Institute of China Electronics Technology Group Corporation, Zhengzhou, 450047, China)

Keywords:: Augmented reality; Artificial intelligence; Time sensitive targets; Object detection; Steady state visual evoked potential; Target recognition

CLC:: R318. 04TP391. 4

DOI:: 10. 13705 / j. issn. 1671-6833. 2025. 01. 017

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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