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One-Shot Learned Priors in Augmented Active Appearance Models for Anatomical Landmark Tracking

  • Oliver MothesEmail author
  • Joachim Denzler
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 983)

Abstract

In motion science, biology and robotics animal movement analyses are used for the detailed understanding of the human bipedal locomotion. For this investigations an immense amount of recorded image data has to be evaluated by biological experts. During this time-consuming evaluation single anatomical landmarks, for example bone ends, have to be located and annotated in each image. In this paper we show a reduction of 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. 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.

Keywords

One-shot learned detector X-ray videography Graph-based landmark tracking Animal locomotion analysis Active appearance models 

Notes

Acknowledgments

The research was supported by grant DE 735/8-3 of the German Research Foundation (DFG).

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Vision GroupFriedrich Schiller University JenaJenaGermany

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