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

• Published in: Journal of toxicology Vol. 2018; no. 2018; pp. 1 - 11
• Main Authors: Xia, Chunlei, Liu, Yuedan, Liu, Zuoyi, Fu, Longwen, Liu, Hui, Chen, L.
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Animal behavior; Aquatic ecosystems; Artificial intelligence; Behavior; Computer applications; Computer vision; Data acquisition; Early warning systems; Ecological risk assessment; Entropy; Environmental stress; Fish behavior; Informatics; Learning algorithms; Machine learning; Machine vision; Pattern analysis; Review; Risk assessment; Risk taking; Sensors; Time series; Toxicity; Toxicology; Tracking; Water quality; Zebrafish
• ISSN: 1687-8191; 1687-8205
• DOI: 10.1155/2018/2591924

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.