GaitMM: Multi-Granularity Motion Sequence Learning for Gait Recognition
Gait recognition aims to identify individual-specific walking patterns by observing the different periodic movements of each body part. However, most existing methods treat each part equally and fail to account for the data redundancy caused by the different step frequencies and sampling rates of ga...
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Zusammenfassung: | Gait recognition aims to identify individual-specific walking patterns by
observing the different periodic movements of each body part. However, most
existing methods treat each part equally and fail to account for the data
redundancy caused by the different step frequencies and sampling rates of gait
sequences. In this study, we propose a multi-granularity motion representation
network (GaitMM) for gait sequence learning. In GaitMM, we design a combined
full-body and fine-grained sequence learning module (FFSL) to explore
part-independent spatio-temporal representations. Moreover, we utilize a
frame-wise compression strategy, referred to as multi-scale motion aggregation
(MSMA), to capture discriminative information in the gait sequence. Experiments
on two public datasets, CASIA-B and OUMVLP, show that our approach reaches
state-of-the-art performances. |
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DOI: | 10.48550/arxiv.2209.08470 |