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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 ISSN 1424-8220.
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Official URL: https://doi.org/10.3390/s20061646
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 | ||||||||||||||||||
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| Subjects: | Q Science > QP Physiology T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > 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: |
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| Volume: | 20 | ||||||||||||||||||
| Number: | 6 | ||||||||||||||||||
| Article Number: | e1646 | ||||||||||||||||||
| DOI: | 10.3390/s20061646 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
| Date of first compliant deposit: | 19 March 2020 | ||||||||||||||||||
| Date of first compliant Open Access: | 23 March 2020 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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