TY - JOUR
T1 - Automatic detection of locomotor play in young pigs
T2 - A proof of concept
AU - Larsen, Mona L.V.
AU - Wang, Meiqing
AU - Willems, Sam
AU - Liu, Dong
AU - Norton, Tomas
N1 - Publisher Copyright:
© 2023 IAgrE
PY - 2023/5
Y1 - 2023/5
N2 - Play behaviour is considered an indicator of animal welfare in young pigs. However, as play behaviour events are short-lasting and occur sporadically, continuous monitoring is necessary. This study presents a first attempt at automatic detection of locomotor play behaviour in young pigs from video by classifying locomotor play from other solitary behaviours including standing, walking, and running. Two methods were developed, compared, and sequentially combined: (1) a less computational heavy method utilising the Gaussian Mixture Model for quantification of movement combined with the calculation of contour features and standard machine learning classifiers (FEATURES); (2) a computational heavy method utilising a deep learning classifier taking both spatial and temporal features into account (DEEP). The DEEP classifier outperformed the FEATURES classifier and obtained values of internal validation recall, precision, and specificity of 94%, 88% and 96%, respectively. When combining the two classification methods, almost similar performance was retained, whilst 44% of the other behaviours were correctly classified without the need for deep learning methods. The combination thereby decreased the computational power needed to run the algorithm. Thus, locomotor play can be automatically detected in young pigs and the combination of a less computational heavy method with a deep learning method can reduce the computational requirements for the classification and detection of complex behaviours. Future work should focus on the segmentation of single pigs during high-speed activity in order to enable the play detection algorithm to work in real-life settings.
AB - Play behaviour is considered an indicator of animal welfare in young pigs. However, as play behaviour events are short-lasting and occur sporadically, continuous monitoring is necessary. This study presents a first attempt at automatic detection of locomotor play behaviour in young pigs from video by classifying locomotor play from other solitary behaviours including standing, walking, and running. Two methods were developed, compared, and sequentially combined: (1) a less computational heavy method utilising the Gaussian Mixture Model for quantification of movement combined with the calculation of contour features and standard machine learning classifiers (FEATURES); (2) a computational heavy method utilising a deep learning classifier taking both spatial and temporal features into account (DEEP). The DEEP classifier outperformed the FEATURES classifier and obtained values of internal validation recall, precision, and specificity of 94%, 88% and 96%, respectively. When combining the two classification methods, almost similar performance was retained, whilst 44% of the other behaviours were correctly classified without the need for deep learning methods. The combination thereby decreased the computational power needed to run the algorithm. Thus, locomotor play can be automatically detected in young pigs and the combination of a less computational heavy method with a deep learning method can reduce the computational requirements for the classification and detection of complex behaviours. Future work should focus on the segmentation of single pigs during high-speed activity in order to enable the play detection algorithm to work in real-life settings.
KW - Animal behaviour
KW - Animal welfare
KW - Computer vision
KW - Gaussian Mixture Model
KW - Precision Livestock Farming
KW - Technology
UR - http://www.scopus.com/inward/record.url?scp=85153492686&partnerID=8YFLogxK
U2 - 10.1016/j.biosystemseng.2023.03.006
DO - 10.1016/j.biosystemseng.2023.03.006
M3 - Journal article
AN - SCOPUS:85153492686
SN - 1537-5110
VL - 229
SP - 154
EP - 166
JO - Biosystems Engineering
JF - Biosystems Engineering
ER -