Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241100155-8.doi: 10.11896/jsjkx.241100155

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Automatic Recognition of Irrelevant Individuals in Videos Based on Multi-object Tracking

MA Yixin1, ZENG Junhao2, YANG Xinyan3, LIANG Gang3   

  1. 1 AVIC Chengdu Aircraft Design & Research Institute,Chengdu 610073,China
    2 No.30 Research Institute of China Electronics Technology Group Corporation,Chengdu 610041,China
    3 School of Cyber Science and Engineering,Sichuan University,Chengdu 610211,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:MA Yixin,born in 1999,postgraduate.Her main research interests include privacy protection,multi-object tracking and aircraft design.
    LIANG Gang,born in 1976,Ph.D,associate professor,master supervisor.His main research interests include network security,online public opinion analysis and prediction,and AI security.
  • Supported by:
    National Natural Science Foundation of China(62162057),National Natural Science Foundation of Sichuan(2025ZNSFSC0509),Sichuan Science and Technology Program(2023YFG0294) and Local Projects of the Ministry of Education(2023CDLZ-2).

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Abstract: Automatic identification of irrelevant individuals aims to detect and identify irrelevant persons in videos to solve their privacy protection issues.Existing privacy protection methods extract high-level visual features to identify individuals irrelevant to the subject.However,the extraction of high-level features significantly affects the processing efficiency of the video and makes it difficult to process massive video data.At the same time,the existing single-frame recognition method does not consider the temporal characteristics of the target,resulting in low accuracy.Therefore,this paper proposes an automatic recognition algorithm to efficiently identify irrelevant individuals,and introduces a multi-target tracking method to determine the correlation between people and videos.The method can extract five lightweight features from the time and space dimensions of the individual’s motion trajectory.In addition,in order to solve the challenges brought by occlusion and blur during video motion,an observation-based trajectory association algorithm is adopted to improve the accuracy of motion tracking.Extensive experiments conducted on various datasets demonstrate that the proposed method achieves significant improvements across multiple evaluation metrics compared to state-of-the-art approaches.Specifically,the MOTA metric shows a maximum improvement of 10.87 percentage points,the HOTA me-tric achieves a maximum increase of 10.95 percentage points,and the accuracy of irrelevant individuals recognition reaches 98.13%.

Key words: Irrelevant individuals recognition, Privacy protection, Multi-object tracking, Face detection, Deep learning

CLC Number: 

  • TP391
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