Robust Indoor Target Tracking Based on Track Matching

Li, Huquan; Xu, Zhenchang; Zhang, Yang; Guo, Shisheng; Cui, Guolong; Kong, Lingjiang

doi:10.1109/radar53847.2021.10028444

Cited by 2 publications

(1 citation statement)

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“…Additionally, for indoor target tracking, where furniture or building structures may cause occlusion, trackers may only produce short trajectories. To address this, Li et al [12] proposed a trajectory-matching approach for indoor-target tracking. It uses Hankel matrix completion techniques to estimate missing data and correlates trajectories based on the rank of the Hankel matrix.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition of Seismic Station Environmental Interference Based on YOLOv5

et al. 2023

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In recent years, human interference in seismic-station environments has posed challenges to the quality and accuracy of seismic signals, making data processing difficult. To accurately identify interference caused by personnel and ensure the reliability of seismic-network instrument detection data, it is necessary to track the detected targets across consecutive frames. Deep neural networks have made significant progress in this field. Therefore, an intelligent identification solution for environmental interference at seismic stations is proposed, which combines deep learning with multi-object tracking techniques. A centroid-matching tracking algorithm based on Kalman filtering is introduced to identify the entry/exit timestamps, alongside motion trajectories of interfering individuals, thereby marking the anomalous data caused by the presence of interfering personnel in seismic time-series data. Experimental results demonstrate that this research provides an effective solution for intelligent identification of environmental interference in seismic station environments.

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Section: Introductionmentioning

confidence: 99%