ArticlePloS one2017
Feature point based 3D tracking of multiple fish from multi-view images.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- A Novel Fish Pose Estimation Method Based on Semi-Supervised Temporal Context Network.Biomimetics (Basel, Switzerland) · 2025Article
- Exploring the use of deep learning models for accurate tracking of 3D zebrafish trajectories.Frontiers in bioengineering and biotechnology · 2024Article
- Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization.Medical & biological engineering & computing · 2023Review
- Comparing behavioral performance and physiological responses of Sebastes schlegelii with different aggressiveness.Fish physiology and biochemistry · 2022Article
- Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas-Kanade Algorithm.Journal of healthcare engineering · 2021Article
- The zebrafish subcortical social brain as a model for studying social behavior disorders.Disease models & mechanisms · 2019Review
- Automatic multiple zebrafish tracking based on improved HOG features.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A feature point based method is proposed for tracking multiple fish in 3D space. First, a simplified representation of the object is realized through construction of two feature point models based on its appearance characteristics. After feature points are classified into occluded and non-occluded types, matching and association are performed, respectively. Finally, the object's motion trajectory in 3D space is obtained through integrating multi-view tracking results. Experimental results show that the proposed method can simultaneously track 3D motion trajectories for up to 10 fish accurately and robustly.
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Registered trials
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