  Existing studies for gait recognition are dominated by in-the-lab scenarios.
Since people live in real-world senses, gait recognition in the wild is a more
practical problem that has recently attracted the attention of the community of
multimedia and computer vision. Current methods that obtain state-of-the-art
performance on in-the-lab benchmarks achieve much worse accuracy on the
recently proposed in-the-wild datasets because these methods can hardly model
the varied temporal dynamics of gait sequences in unconstrained scenes.
Therefore, this paper presents a novel multi-hop temporal switch method to
achieve effective temporal modeling of gait patterns in real-world scenes.
Concretely, we design a novel gait recognition network, named Multi-hop
Temporal Switch Network (MTSGait), to learn spatial features and multi-scale
temporal features simultaneously. Different from existing methods that use 3D
convolutions for temporal modeling, our MTSGait models the temporal dynamics of
gait sequences by 2D convolutions. By this means, it achieves high efficiency
with fewer model parameters and reduces the difficulty in optimization compared
with 3D convolution-based models. Based on the specific design of the 2D
convolution kernels, our method can eliminate the misalignment of features
among adjacent frames. In addition, a new sampling strategy, i.e., non-cyclic
continuous sampling, is proposed to make the model learn more robust temporal
features. Finally, the proposed method achieves superior performance on two
public gait in-the-wild datasets, i.e., GREW and Gait3D, compared with
state-of-the-art methods.
