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Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis

Research output: Contribution to Journal/MagazineConference articlepeer-review

<mark>Journal publication date</mark>31/12/2019
<mark>Journal</mark>Procedia Computer Science
Number of pages12
Pages (from-to)1375-1386
Publication StatusPublished
Early online date14/10/19
<mark>Original language</mark>English


The paper describes the application of local binary patterns and cascade AdaBoost classifier (CAC) to detect and analyse mice behavioural movement. This was done with a view to investigating the inconsistencies associated with current practices, whereby mice behavioural classification is achieved by means of human-generated labels. The developed cascade AdaBoost algorithm was able to detect eight different mice movement, and we develop a system that allows mice behavioural analysis in videos, with minimal supervision. Evaluating the results on Completeness, Consistency and Correctness, and based on the devised analysis, a solution was deployed, showing that machine learning plays an important role in translating video data into scientific knowledge. This is a useful addition to the animal behaviourist's analytical toolkit.