Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

Journal of Toxicology
Chunlei XiaYuedan Liu

Abstract

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

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Citations

Oct 18, 2020·Nanomaterials·Anastasios G PapadiamantisAntreas Afantitis
May 29, 2021·The Science of the Total Environment·Adam Bownik, Donald Wlodkowic
Jan 20, 2020·Ecotoxicology and Environmental Safety·Xiaofei BoMaoxia He

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Methods Mentioned

BETA
chemical stress
imaging techniques
chemical treatments
feature extraction
chemical treatment
feature
PCA

Software Mentioned

Ctrax
HMM
SOM
idTracker

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