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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21638
Title: ELM-HTM guided bio-inspired unsupervised learning for anomalous trajectory classification
Authors: Sekh A.A.
Dogra D.P.
Kar S.
Pratim Roy, Partha
Prasad D.K.
Published in: Cognitive Systems Research
Abstract: Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose a new bio-inspired learning model for a single-class classifier to detect abnormality in video object trajectories. The method uses a simple but dynamic extreme learning machine (ELM) and hierarchical temporal memory (HTM) together referred to as ELM-HTM in an unsupervised way to learn and classify time series patterns. The method has been tested on trajectory sequences in traffic surveillance to find abnormal behaviors such as high-speed, unusual stops, driving in wrong directions, loitering, etc. Experiments have also been performed with 3D air signatures captured using sensors and used for biometric authentication(forged/genuine). The results indicate a significant gain over training time and classification accuracy. The proposed method outperforms in predicting long-time patterns by observing small steps with an average accuracy gain of 15% as compared to the state-of-the-art HTM. The method has applications in detecting abnormal activities in videos by learning the movement patterns as well as in biometric authentication. © 2020 The Author(s)
Citation: Cognitive Systems Research, 63: 30-41
URI: https://doi.org/10.1016/j.cogsys.2020.04.003
http://repository.iitr.ac.in/handle/123456789/21638
Issue Date: 2020
Publisher: Elsevier B.V.
Keywords: Anomaly detection
Bio-inspired learning
ELM
HTM
Trajectory analysis
ISSN: 13890417
Author Scopus IDs: 57216801662
35408975400
57214748469
56880478500
35746873900
Author Affiliations: Sekh, A.A., Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, 9019, Norway
Dogra, D.P., School of Electrical Science, Indian Institute of Technology Bhubaneswar, Bhubaneswar, 751013, India
Kar, S., Department of Mathematics, National Institute of Technology Durgapur, Durgapur, 713209, India
Roy, P.P., Department of Computer Science, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India
Prasad, D.K., Department of Computer Science, UiT The Arctic University of Norway, Tromsø, 9019, Norway
Corresponding Author: Sekh, A.A.; Department of Physics and Technology, Norway; email: arif.ahmed.sekh@uit.no
Appears in Collections:Journal Publications [CS]

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