GaitPart: Temporal Part-Based Model for Gait Recognition
By Chao Fan, Yunjie Peng, Chunshui Cao, Xu Liu, Saihui Hou, Jiannan Chi, Yongzhen Huang, Qing Li, Zhiqiang He. Computer Vision and Pattern Recognition, 2020
552 citations recorded by the source corpus.
Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies. At present, most gait recognition methods take the whole human body as a unit to establish the spatio-temporal representations. However, we have observed that different parts of human body possess evidently various visual appearances and movement patterns during walking. In the latest literature, employing partial features for human body description has been verified being beneficial to individual recognition. Taken above insights together, we assume that each part of human body needs its own spatio-temporal expression. Then, we propose a novel part-based model GaitPart and get two aspects effect of boosting the performance: On the one hand, Focal Convolution Layer, a new applying of convolution, is presented to enhance the fine-grained learning of the part-level spatial features. On the other hand, the Micro-motion Capture Module (MCM) is proposed and there are several parallel MCMs in the GaitPart corresponding to the pre-defined parts of the human body, respectively. It is worth mentioning that the MCM is a novel way of temporal modeling for gait task, which focuses on the short-range temporal features rather than the redundant long-range features for cycle gait. Experiments on two of the most popular public datasets, CASIA-B and OU-MVLP, richly exemplified that our method meets a new state-of-the-art on multiple standard benchmarks. The source code will be available on https://github.com/ChaoFan96/GaitPart.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism
View Paper: https://www.semanticscholar.org/paper/2b88f9137442a1b8e0626c0d4ce78b128c12f453
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In Biomedical Engineering
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Citations in the corpus
- Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition (2021, methodology): “GaitPart [3] introduces a novel temporal module, that focuses on capturing short-range temporal features.”
- Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation (2020, methodology): “Fan et al.[5] develop a focal convolutional layer to extract the local features and then combine them.”