English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Spatiotemporal deformable prototypes for motion anomaly detection

Bensch, R., Scherf, N., Huisken, J., Brox, T., & Ronneberger, O. (2017). Spatiotemporal deformable prototypes for motion anomaly detection. International Journal of Computer Vision, 122, 502-523. doi:10.1007/s11263-016-0934-1.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Bensch, Robert1, Author
Scherf, Nico1, Author              
Huisken, Jan1, Author
Brox, Thomas1, Author
Ronneberger, Olaf1, Author
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: Anomaly detection; Motion patterns; Point trajectories; Elastic registration
 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.

Details

show
hide
Language(s): eng - English
 Dates: 2016-01-152016-07-062016-07-192017-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/s11263-016-0934-1
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: International Journal of Computer Vision
  Other : Int. J. Comput. Vis.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Hingham, Mass. : Kluwer Academic Publishers
Pages: - Volume / Issue: 122 Sequence Number: - Start / End Page: 502 - 523 Identifier: ISSN: 0920-5691
CoNE: https://pure.mpg.de/cone/journals/resource/954925564668