Identifying lameness in horses through deep learning

Animal pose estimation is important to understand animal behavior and injury. This is especially the case with horse lameness. In the past, studies have focused on combining trackable markers on animals with high-speed cameras to support gait detection. This is time consuming and often impractical. This paper explores the feasibility of using deep learning for markerless motion tracking. We trained a neural network to locate a horse's body parts, then analyzed whether the horse was lame based on their motion trajectory. Different animal pose estimation methods were studied using two state of the art convolutional network models. This paper presents the results of this work. A mobile application was also developed to test the practical application of marker-less recognition technology for detecting lameness in horses.