  Dog owners are typically capable of recognizing behavioral cues that reveal
subjective states of their dogs, such as pain. But automatic recognition of the
pain state is very challenging. This paper proposes a novel video-based,
two-stream deep neural network approach for this problem. We extract and
preprocess body keypoints, and compute features from both keypoints and the RGB
representation over the video. We propose an approach to deal with
self-occlusions and missing keypoints. We also present a unique video-based dog
behavior dataset, collected by veterinary professionals, and annotated for
presence of pain, and report good classification results with the proposed
approach. This study is one of the first works on machine learning based
estimation of dog pain state.
