Research on Machine Vision–Based Intelligent Tracking System for Maintenance Personnel - Yinglin Ma; Hongmei Shi; Yao Wang; Baofeng Li (Journal of Advanced Transportation)
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.