Walking Further: Semantic-aware Multimodal Gait Recognition Under Long-Range Conditions

• URL: http://arxiv.org/abs/2603.14189v1
• Date: Sun, 15 Mar 2026 02:32:27 GMT
• Title: Walking Further: Semantic-aware Multimodal Gait Recognition Under Long-Range Conditions
• Abstract summary: We present textbfLRGait, the first LiDAR-Camera multimodal benchmark for robust long-range gait recognition.<n>We also propose textbfEMGaitNet, an end-to-end framework tailored for long-range multimodal gait recognition.
• Score: 27.59550056623308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition is an emerging biometric technology that enables non-intrusive and hard-to-spoof human identification. However, most existing methods are confined to short-range, unimodal settings and fail to generalize to long-range and cross-distance scenarios under real-world conditions. To address this gap, we present \textbf{LRGait}, the first LiDAR-Camera multimodal benchmark designed for robust long-range gait recognition across diverse outdoor distances and environments. We further propose \textbf{EMGaitNet}, an end-to-end framework tailored for long-range multimodal gait recognition. To bridge the modality gap between RGB images and point clouds, we introduce a semantic-guided fusion pipeline. A CLIP-based Semantic Mining (SeMi) module first extracts human body-part-aware semantic cues, which are then employed to align 2D and 3D features via a Semantic-Guided Alignment (SGA) module within a unified embedding space. A Symmetric Cross-Attention Fusion (SCAF) module hierarchically integrates visual contours and 3D geometric features, and a Spatio-Temporal (ST) module captures global gait dynamics. Extensive experiments on various gait datasets validate the effectiveness of our method.

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