Upon returning to the depot, rail transit vehicles require necessary maintenance. The working condition of train maintenance personnel directly impacts the safety of both staff and equipment. Therefore, effective monitoring and control of activities within train roof access platforms are essential. Traditional manual monitoring demands substantial manpower and is prone to human error, whereas machine vision–based intelligent monitoring offers a promising alternative, reducing the dispatch control center (DCC) workload while enhancing safety management. Our intelligent monitoring approach involves three key steps: train maintenance personnel identification, tracking of maintenance activities to generate movement trajectories, and analysis of movement patterns to detect anomalous behavior. This study primarily addresses the challenges of personnel identification and process tracking. In the scenario of train maintenance, facial recognition is limited by posture variations, making direct video tracking impractical. Pedestrian reidentification (Re-ID) also struggles with posture and attire changes. To address these issues, we propose a hybrid approach: facial recognition confirms personnel identity upon entry, followed by pedestrian feature extraction for Re-ID-based tracking throughout the maintenance process. To handle occlusion, we designed a Re-ID method based on body part recognition, segmenting features into head–shoulder, body, arm, and leg components, with higher weights assigned to visible parts. This method achieved improved mean average precision (mAP) and Rank-1 values of 87.6% and 95.7%, respectively, on the Market1501 dataset. A tracking and monitoring system was developed, effectively identifying and tracking maintenance activities, demonstrating a strong practical value. Furthermore, this work lays the groundwork for future research into trajectory-based abnormal behavior detection.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Research on Machine Vision–Based Intelligent Tracking System for Maintenance Personnel


    Beteiligte:
    Yinglin Ma (Autor:in) / Hongmei Shi (Autor:in) / Yao Wang (Autor:in) / Baofeng Li (Autor:in)


    Erscheinungsdatum :

    2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Intelligent system for guaranteeing safety of road maintenance personnel

    LONG KEJUN / LYU CHAN / ZHANG RONGYUE et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    TRACK MAINTENANCE PERSONNEL MANAGEMENT SYSTEM

    OKAMOTO SHINJI / MORI KENICHI / SASAKI TORU | Europäisches Patentamt | 2017

    Freier Zugriff

    Research on Path Tracking Control for Vision Based Intelligent Vehicle

    Cui, Sheng Min ;Zhang, Chao ;Wang, Jian Feng | Trans Tech Publications | 2011


    Maintenance management of personnel transport system

    PICANDER OLIVER / ZAKRZEWSKI MAREK | Europäisches Patentamt | 2025

    Freier Zugriff

    Urban intelligent traffic monitoring intelligent system based on machine vision

    HU XINKE | Europäisches Patentamt | 2021

    Freier Zugriff