Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Conference article › peer-review
<mark>Journal publication date</mark> | 31/12/2019 |
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<mark>Journal</mark> | Procedia Computer Science |
Volume | 159 |
Number of pages | 12 |
Pages (from-to) | 1375-1386 |
Publication Status | Published |
Early online date | 14/10/19 |
<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.