Deep cascaded convolutional models for cattle pose estimation

Cattle pose estimation is a key step analyzing cattle behaviors and evaluating cattle health, hence, greatly significant for intelligent breeding of cattle. Computer vision based on cattle pose automatic estimation techniques were investigated in this paper and three deep cascaded convolutional neural networks models, including the convolutional pose machine model, the stacked hourglass model and the convolutional heatmap regression model were developed to perform robust cattle pose estimation, with RGB images captured under real cattle farm conditions. A cattle image dataset was also constructed for data modeling and method evaluation, which contains 2134 images of 33 dairy cattle and 30 beef cattle with various poses under natural conditions. In order to enlarge the dataset and decrease the chances of overfitting, data augmentation techniques such as image horizontal flip, rotation and color conversion were used in the data training process of these deep models. The experimental results show that the stacked hourglass model has achieved better performance than the other two, reaching a 90.39% PCKh mean score at the threshold of 0.5 for 16 joints. The high detection accuracy would make the model a very helpful tool for the subsequent cattle behavior recognition and understanding.