Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Sensors, vol. 21, pp. 2114

Abstract

Reliable object tracking that is based on video data constitutes an important challenge in diverse areas, including, among others, assisted surgery. Particle filtering offers a state-of-the-art technology for this challenge. Becaise a particle filter is based on a probabilistic model, it provides explicit likelihood values; in theory, the question of whether an object is reliably tracked can be addressed based on these values, provided that the estimates are correct. In this contribution, we investigate the question of whether these likelihood values are suitable for deciding whether the tracked object has been lost. An immediate strategy uses a simple threshold value to reject settings with a likelihood that is too small. We show in an application from the medical domain-object tracking in assisted surgery in the domain of Robotic Osteotomies-that this simple threshold strategy does not provide a reliable reject option for object tracking, in particular if different settings are considered. However, it is possible to develop reliable and flexible machine learning models that predict a reject based on diverse quantities that are computed by the particle filter. Modeling the task in the form of a regression enables a flexible handling of different demands on the tracking accuracy; modeling the challenge as an ensemble of classification tasks yet surpasses the results, while offering the same flexibility.

Authors 6

1. Johannes Kummert corresponding

   Bielefeld University

   Affiliation as printed

   Machine Learning Group, Bielefeld University, 33619 Bielefeld, Germany

2. Alexander Schulz

   Bielefeld University

   Affiliation as printed

   Machine Learning Group, Bielefeld University, 33619 Bielefeld, Germany

3. Tim Redick Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, 52074 Aachen, Germany

4. Ali Modabber Aachen Department of Oral and Maxillofacial Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany

5. Dirk Abel Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, 52074 Aachen, Germany

6. Barbara Hammer

   Bielefeld University

   Affiliation as printed

   Machine Learning Group, Bielefeld University, 33619 Bielefeld, Germany