Improving 3-D Zebrafish Tracking With Multiview Data Fusion and Global Association

Cui Wang, Zewei Wu, Yanbing Chen, Wei Zhang, Wei Ke, Zhang Xiong

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Zebrafish behavioral patterns reveal valuable insights for biomedical research. To accurately identify these patterns, visual tracking systems need to reconstruct 3-D trajectories from multiview video sequences. However, 3-D zebrafish tracking faces challenges such as the dynamics in movements, the similarity in appearances, and the distortion caused by different viewpoints. In this article, we propose a new method for robust 3-D zebrafish trajectory reconstruction based on multiview data fusion and global association. Our method generates reliable segments of 2-D/3-D trajectories, called tracklets, where we consider short-term cues of appearance similarity and motion consistency and propose corresponding scoring metrics. Moreover, we use a lazy-reconstruction strategy to enhance the overall accuracy of 3-D trajectories by taking into account the global context. Extensive experiments on the public 3D-ZeF20 dataset demonstrate the effectiveness of the proposed method, achieving 67.9% multiple object tracking accuracy (MOTA), 64.3% ID F1 Score (IDF1), and 55.0 MTBFm.

Original languageEnglish
Pages (from-to)17245-17259
Number of pages15
JournalIEEE Sensors Journal
Issue number15
Publication statusPublished - 1 Aug 2023


  • 3-D trajectory measurement
  • detection data fusion
  • multiview multiple object tracking
  • zebrafish behavioral trajectory reconstruction


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