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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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
Mountcastle, V.B.: The columnar organization of the neocortex. Brain: J. Neurol. 120(4), 701–722 (1997)
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)
Fallas-Moya, F.: Object tracking based on hierarchical temporal memory classification (2015)
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)
Mabrouk, A.B., Zagrouba, E.: Abnormal behavior recognition for intelligent video surveillance systems a review. Exp. Syst. Appl. 91, 480–491 (2017)
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)
Ren, L., et al.: A data-driven approach to quantifying natural human motion. ACM Trans. Graph. (TOG) 24(3), 1090–1097 (2005)
CMU Graphics Lab: Carnegie Mellon University Motion Capture Database (2003). http://mocap.cs.cmu.edu/
Numenta: NuPIC (2018). https://github.com/numenta/nupic
Numenta: NuPIC.vision (2017). https://github.com/numenta/nupic.vision
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic Routing Between Capsules. CoRR abs/1710.09829 (2017). arXiv:1710.09829
Yarbus, A.L.: Eye movements during perception of complex objects. In: Eye Movements and Vision, pp. 171–211. Springer (1967)
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
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
DOI: https://doi.org/10.1007/978-3-319-99316-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99315-7
Online ISBN: 978-3-319-99316-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)