Extracting statistically significant behaviour from fish tracking data with and without large dataset cleaning. Issue 2 (18th December 2017)

• Record Type: Journal Article
• Authors: Beyan, Cigdem; Katsageorgiou, Vasiliki‐Maria; Fisher, Robert B
• Is Part Of: IET computer vision. Volume 12:Issue 2(2018)
• Page Start: 162
• Page End: 170
• Publication Date: 2017-12-18
• Subjects: image denoising -- pattern classification -- aquaculture -- data handling -- estimation theory -- learning (artificial intelligence) -- computer vision; statistical extraction -- fish tracking data -- dataset cleaning -- computer vision -- natural phenomena -- image data -- data binning; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2016.0462
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Extracting a statistically significant result from video of natural phenomenon can be difficult for two reasons: (i) there can be considerable natural variation in the observed behaviour and (ii) computer vision algorithms applied to natural phenomena may not perform correctly on a significant number of samples. This study presents one approach to clean a large noisy visual tracking dataset to allow extracting statistically sound results from the image data. In particular, analyses of 3.6 million underwater trajectories of a fish with the water temperature at the time of acquisition are presented. Although there are many false detections and incorrect trajectory assignments, by a combination of data binning and robust estimation methods, reliable evidence for an increase in fish speed as water temperature increases are demonstrated. Then, a method for data cleaning which removes outliers arising from false detections and incorrect trajectory assignments using a deep learning‐based clustering algorithm is proposed. The corresponding results show a rise in fish speed as temperature goes up. Several statistical tests applied to both cleaned and not‐cleaned data confirm that both results are statistically significant and show an increasing trend. However, the latter approach also generates a cleaner dataset suitable for other analysis.