ArticleScientific reports2018
Automatic multiple zebrafish tracking based on improved HOG features.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- High-throughput multi-camera array microscope platform for automated 3D behavioral analysis of swimming zebrafish larvae.Communications biology · 2026Article
- High-throughput multi-camera array microscope platform for automated 3D behavioral analysis of swimming zebrafish larvae.bioRxiv : the preprint server for biology · 2025Article
- Synergistic enhancement of detection-tracking framework for zebrafish shoaling behavior analysis.Scientific reports · 2025Article
- Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning.Neuroscience bulletin · 2023Article
- Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking across Non-Overlapping Multiple Cameras.Sensors (Basel, Switzerland) · 2022Article
- Zebrafish automatic monitoring system for conditioning and behavioral analysis.Scientific reports · 2021Article
- Zebrafish tracking using YOLOv2 and Kalman filter.Scientific reports · 2021Article
- Current Methods to Investigate Nociception and Pain in Zebrafish.Frontiers in neuroscience · 2021Review
- The zebrafish subcortical social brain as a model for studying social behavior disorders.Disease models & mechanisms · 2019Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As an excellent model organism, zebrafish have been widely applied in many fields. The accurate identification and tracking of individuals are crucial for zebrafish shoaling behaviour analysis. However, multi-zebrafish tracking still faces many challenges. It is difficult to keep identified for a long time due to fish overlapping caused by the crossings. Here we proposed an improved Histogram of Oriented Gradient (HOG) algorithm to calculate the stable back texture feature map of zebrafish, then tracked multi-zebrafish in a fully automated fashion with low sample size, high tracking accuracy and wide applicability. The performance of the tracking algorithm was evaluated in 11 videos with different numbers and different sizes of zebrafish. In the Right-tailed hypothesis test of Wilcoxon, our method performed better than idTracker, with significant higher tracking accuracy. Throughout the video of 16 zebrafish, the training sample of each fish had only 200-500 image samples, one-fifth of the idTracker's sample size. Furthermore, we applied the tracking algorithm to analyse the depression and hypoactivity behaviour of zebrafish shoaling. We achieved correct identification of depressed zebrafish among the fish shoal based on the accurate tracking results that could not be identified by a human.
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