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Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

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Luo, Jian and Tjahjadi, Tardi (2020) Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding. Sensors, 20 (6). e1646. doi:10.3390/s20061646

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Official URL: https://doi.org/10.3390/s20061646

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Abstract

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness.

Item Type: Journal Article
Subjects: Q Science > QP Physiology
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science > Engineering
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Biometric identification, Gait in humans -- Computer simulation, Pattern recognition systems, Computer vision
Journal or Publication Title: Sensors
Publisher: MDPI
ISSN: 1424-8220
Official Date: 16 March 2020
Dates:
DateEvent
16 March 2020Published
13 March 2020Accepted
Date of first compliant deposit: 19 March 2020
Volume: 20
Number: 6
Article Number: e1646
DOI: 10.3390/s20061646
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
61701179[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
41604117[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
2018TP1018Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Provincehttp://dx.doi.org/10.13039/501100012269
20180843028China  Scholarship  Councilhttp://viaf.org/viaf/7605150567632306370007
2019JJ50363Natural Science Foundation of Hunan Provincehttp://dx.doi.org/10.13039/501100004735
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