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Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

by Jian Luo 1,* and Tardi Tjahjadi 2
1
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410000, China
2
School of Engineering, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1646; https://doi.org/10.3390/s20061646
Received: 22 January 2020 / Revised: 11 March 2020 / Accepted: 13 March 2020 / Published: 16 March 2020
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: gait recognition; human identification; hierarchical temporal memory; semantic folding gait recognition; human identification; hierarchical temporal memory; semantic folding
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Luo, J.; Tjahjadi, T. Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding. Sensors 2020, 20, 1646.

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