ArticleScientific reports2024
Space dynamic target tracking method based on five-frame difference and Deepsort.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Hybrid neural network for personnel recognition and tracking in remote bidding evaluation monitoring.Scientific reports · 2026Article
- Oblique-view video tracking and density-based counting: accurate counting of late-stage rapeseed seedlings for breeding assessment.Frontiers in plant science · 2026Article
- CEHD: A Unified Framework for Detection and Height Estimation of Fresh Corn Ears in Field Conditions.Plants (Basel, Switzerland) · 2025Article
- Optimizing Helmet Use Detection in Construction Sites via Fuzzy Logic-Based State Tracking.Sensors (Basel, Switzerland) · 2025Article
- Computer vision-based laser communication system for robust optical beam tracking and alignment.Scientific reports · 2025Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
For the problem of space dynamic target tracking with occlusion, this paper proposes an online tracking method based on the combination between the five-frame difference and Deepsort (Simple Online and Realtime Tracking with a Deep Association Metric), which is to achieve the identification first and then tracking of the dynamic target. First of all, according to three-frame difference, the five-frame difference is improved, and through the integration with ViBe (Visual Background Extraction), the accuracy and anti-interference ability are enhanced; Secondly, the YOLOv5s (You Look Only Once) is improved using preprocessing of DWT (Discrete Wavelet Transformation) and injecting GAM (Global Attention Module), which is considered as the detector for Deepsort to solve the missing in occlusion, and the real-time and accuracy can be strengthened; Lastly, simulation results show that the proposed space dynamic target tracking can keep stable to track all dynamic targets under the background interference and occlusion, the tracking precision is improved to 93.88%. Furthermore, there is a combination with the physical depth camera D435i, experiments on target dynamics show the effectiveness and superiority of the proposed recognition and tracking algorithm in the face of strong light and occlusion.
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Registered trials
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