Advances in Intelligent Systems and Computing, volume 848, pages 69-81
Extended Hierarchical Temporal Memory for Motion Anomaly Detection
Publication type: Book Chapter
Publication date: 2018-08-24
Quartile SCImago
— Quartile WOS
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Impact factor: —
ISSN: 21945357
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.
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Lecture Notes in Computer Science
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Lecture Notes in Computer Science
1 publication, 25%
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Brain Informatics
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Brain Informatics
1 publication, 25%
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Computational Intelligence and Neuroscience
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Computational Intelligence and Neuroscience
1 publication, 25%
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Neural Processing Letters
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Neural Processing Letters
1 publication, 25%
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Springer Nature
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Springer Nature
3 publications, 75%
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Hindawi Limited
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Hindawi Limited
1 publication, 25%
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Daylidyonok I. et al. Extended Hierarchical Temporal Memory for Motion Anomaly Detection // Advances in Intelligent Systems and Computing. 2018. Vol. 848. pp. 69-81.
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Daylidyonok I., Frolenkova A., Panov A. I. Extended Hierarchical Temporal Memory for Motion Anomaly Detection // Advances in Intelligent Systems and Computing. 2018. Vol. 848. pp. 69-81.
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TY - GENERIC
DO - 10.1007/978-3-319-99316-4_10
UR - https://doi.org/10.1007%2F978-3-319-99316-4_10
TI - Extended Hierarchical Temporal Memory for Motion Anomaly Detection
T2 - Advances in Intelligent Systems and Computing
AU - Daylidyonok, Ilya
AU - Frolenkova, Anastasiya
AU - Panov, Aleksandr I
PY - 2018
DA - 2018/08/24 00:00:00
PB - Springer Nature
SP - 69-81
VL - 848
SN - 2194-5357
ER -
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@incollection{2018_Daylidyonok,
author = {Ilya Daylidyonok and Anastasiya Frolenkova and Aleksandr I Panov},
title = {Extended Hierarchical Temporal Memory for Motion Anomaly Detection},
publisher = {Springer Nature},
year = {2018},
volume = {848},
pages = {69--81},
month = {aug}
}
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