Anatomical Landmark Tracking by One-shot Learned Priors for Augmented Active Appearance Models

For animal bipedal locomotion analysis, an immense amount of recorded image data has to be evaluated by biological experts. During this time-consuming evaluation single anatomical landmarks have to be annotated in each image. In this paper we reduce this effort by automating the annotation with a minimum level of user interaction. Recent approaches, based on Active Appearance Models, are improved by priors based on anatomical knowledge and an online tracking method, requiring only a single labeled frame. However, the limited search space of the online tracker can lead to a template drift in case of severe self-occlusions. In contrast, we propose a one-shot learned tracking-by-detection prior which overcomes the shortcomings of template drifts without increasing the number of training data. We evaluate our approach based on a variety of real-world X-ray locomotion datasets and show that our method outperforms recent state-of-the-art concepts for the task at hand.