Playing Behavior Classification of Group-Housed Pigs Using a Deep CNN-LSTM Network

Abstract

The swine industry is one of the industries that progressively incorporates smart livestock farming (SLF) to monitor the grouped-housed pigs’ welfare. In recent years, pigs’ positive welfare has gained much attention. One of the evident behavioral indicators of positive welfare is playing behaviors. However, playing behavior is spontaneous and temporary, which makes the detection of playing behaviors difficult. The most direct method to monitor the pigs’ behaviors is a video surveillance system, for which no comprehensive classification framework exists. In this work, we develop a comprehensive pig playing behavior classification framework and build a new video-based classification model of pig playing behaviors using deep learning. We base our deep learning framework on an end-to-end trainable CNN-LSTM network, with ResNet34 as the CNN backbone model. With its high classification accuracy of over 92% and superior performances over the existing models, our proposed model highlights the importance of applying the global maximum pooling method on the CNN final layer’s feature map and leveraging a temporal attention layer as an input to the fully connected layer for final prediction. Our work has direct implications on advancing the welfare assessment of group-housed pigs and the current practice of SLF.