International Journal of Computer Vision

, Volume 122, Issue 3, pp 502–523 | Cite as

Spatiotemporal Deformable Prototypes for Motion Anomaly Detection

  • Robert Bensch
  • Nico Scherf
  • Jan Huisken
  • Thomas Brox
  • Olaf Ronneberger
Article

Abstract

This paper presents an approach for motion-based anomaly detection, where a prototype pattern is detected and elastically registered against a test sample to detect anomalies in the test sample. The prototype model is learned from multiple sequences to define accepted variations. “Supertrajectories” based on hierarchical clustering of dense point trajectories serve as an efficient and robust representation of motion patterns. An efficient hashing approach provides transformation hypotheses that are refined by a spatiotemporal elastic registration. We propose a new method for elastic registration of 3D+time trajectory patterns that induces spatial elasticity from trajectory affinities. The method is evaluated on a new motion anomaly dataset of juggling patterns and performs well in detecting subtle anomalies. Moreover, we demonstrate the applicability to biological motion patterns.

Keywords

Anomaly detection Motion patterns Point trajectories Elastic registration 

Supplementary material

Supplementary material 1 (m4v 22775 KB)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany
  2. 2.BIOSS Centre for Biological Signalling StudiesUniversity of FreiburgFreiburgGermany
  3. 3.Max Planck Institute of Molecular Cell Biology and GeneticsDresdenGermany
  4. 4.Faculty of Medicine Carl Gustav Carus, Institute for Medical Informatics and BiometryTU DresdenDresdenGermany

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