Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning

TL;DR

This paper introduces a novel motion modeling technique for cross-view gait recognition that enhances feature extraction from gait sequences, improving robustness against viewpoint and clothing variations.

Contribution

It proposes a motion modeling approach that can be integrated with any backbone network, demonstrated with GaitGL, to significantly boost gait recognition accuracy.

Findings

Outperforms existing state-of-the-art methods on cross-view gait datasets.

Effective in extracting discriminative motion features from gait sequences.

Independent of specific network architectures, versatile for various models.

Abstract

As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security, and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extracting diverse motion features, a fundamental characteristic in gaits, from gait sequences. This paper proposes a novel motion modeling method to extract the discriminative and robust representation. Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer. Then we continuously enhance the motion feature in deep layers. This motion modeling approach is independent of mainstream work in building network architectures. As a result, one can apply this motion modeling method to any backbone to improve gait recognition performance. In this paper, we combine motion modeling with one commonly used backbone~(GaitGL) as GaitGL-M to illustrate motion modeling. Extensive experimental results on two commonly-used cross-view gait datasets demonstrate the superior performance of GaitGL-M over existing state-of-the-art methods.