Skip to main content

Extended Hierarchical Temporal Memory for Motion Anomaly Detection

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 848)

Abstract

This paper describes the application of hierarchical temporal memory (HTM) to the task of anomaly detection in human motions. A number of model experiments with well-known motion dataset of Carnegie Mellon University have been carried out. An extended version of HTM is proposed, in which feedback on the movement of the sensor’s focus on the video frame is added, as well as intermediate processing of the signal transmitted from the lower layers of the hierarchy to the upper ones. By using elements of reinforcement learning and feedback on focus movement, the HTM’s temporal pooler includes information about the next position of focus, simulating the human saccadic movements. Processing the output of the temporal memory stabilizes the recognition process in large hierarchies.

Keywords

  • Anomaly detection
  • Hierarchical temporal memory
  • Video processing
  • HTM feedback
  • Hierarchical learning

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-99316-4_10
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-99316-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Hawkins, J., Blakeslee, S.: On Intelligence: How a New Understanding of the Brain will lead to the Creation of Truly Intelligent Machines. Macmillan, Noida (2007)

    Google Scholar 

  2. Hawkins, J., Ahmad, S., Dubinsky, D.: Hierarchical temporal memory including HTM cortical learning algorithms. Techical report, Numenta, Inc, Palto Alto (2010). http://www.numenta.com/htmoverview/education/HTM_CorticalLearningAlgorithms.pdf

  3. Mountcastle, V.B.: The columnar organization of the neocortex. Brain: J. Neurol. 120(4), 701–722 (1997)

    CrossRef  Google Scholar 

  4. Wielgosz, M., Pietroń, M., Wiatr, K.: Using spatial pooler of hierarchical temporal memory for object classification in noisy video streams. In: 2016 Federated Conference on Computer Science and Information Systems (FedC- SIS), pp. 271–274. IEEE (2016)

    Google Scholar 

  5. Fallas-Moya, F.: Object tracking based on hierarchical temporal memory classification (2015)

    Google Scholar 

  6. Lavin, A., Ahmad, S.: Evaluating real-time anomaly detection algorithms-the numenta anomaly benchmark. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 38–44. IEEE (2015)

    Google Scholar 

  7. Mabrouk, A.B., Zagrouba, E.: Abnormal behavior recognition for intelligent video surveillance systems a review. Exp. Syst. Appl. 91, 480–491 (2017)

    CrossRef  Google Scholar 

  8. Suzuki, N., et al.: Learning motion patterns and anomaly detection by human trajectory analysis. In: 2007 IEEE International Conference on Systems, Man and Cybernetics. ISIC, pp. 498–503. IEEE (2007)

    Google Scholar 

  9. Ren, L., et al.: A data-driven approach to quantifying natural human motion. ACM Trans. Graph. (TOG) 24(3), 1090–1097 (2005)

    CrossRef  Google Scholar 

  10. CMU Graphics Lab: Carnegie Mellon University Motion Capture Database (2003). http://mocap.cs.cmu.edu/

  11. Numenta: NuPIC (2018). https://github.com/numenta/nupic

  12. Numenta: NuPIC.vision (2017). https://github.com/numenta/nupic.vision

  13. Sabour, S., Frosst, N., Hinton, G.E.: Dynamic Routing Between Capsules. CoRR abs/1710.09829 (2017). arXiv:1710.09829

  14. Yarbus, A.L.: Eye movements during perception of complex objects. In: Eye Movements and Vision, pp. 171–211. Springer (1967)

    Google Scholar 

Download references

Acknowledgments

This work was supported by Russian Foundation for Basic Research (Project No. 16-37-60055 and 17-29-07051).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aleksandr I. Panov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Daylidyonok, I., Frolenkova, A., Panov, A.I. (2019). Extended Hierarchical Temporal Memory for Motion Anomaly Detection. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2018. BICA 2018. Advances in Intelligent Systems and Computing, vol 848. Springer, Cham. https://doi.org/10.1007/978-3-319-99316-4_10

Download citation