Bidirectional Tracking Method for Construction Workers in Dealing with Identity Errors

Abstract

Online multi-object tracking (MOT) techniques are instrumental in monitoring workers’ positions and identities in construction settings. Traditional approaches, which employ deep neural networks (DNNs) for detection followed by body similarity matching, often overlook the significance of clear head features and stable head motions. This study presents a novel bidirectional tracking method that integrates intra-frame processing, which combines head and body analysis to minimize false positives and inter-frame matching to control ID assignment. By leveraging head information for enhanced body tracking, the method generates smoother trajectories with reduced ID errors. The proposed method achieved a state-of-the-art (SOTA) performance, with a multiple-object tracking accuracy (MOTA) of 95.191%, higher-order tracking accuracy (HOTA) of 78.884% and an identity switch (IDSW) count of 0, making it a strong baseline for future research.

Keywords

• multi-object tracking
• worker tracking
• head-integrated
• intra-frame processing
• inter-frame matching
• Kalman filter