Watch Where You Move: Region-aware Dynamic Aggregation and Excitation for Gait Recognition

2025-10-18

AI Summary

Existing gait recognition methods rely on predefined spatial regions and fixed temporal scales, limiting their ability to model the dynamic evolution of motion regions and robustly handle covariate shifts (e.g., viewpoint variations, carried objects). To address these limitations, we propose the Region-aware Dynamic Aggregation and Excitation framework (RDA-RDE), the first approach enabling differentiable automatic discovery of motion-relevant regions, adaptive temporal-scale assignment, and region-specific receptive field modulation. Specifically, the Dynamic Temporal Aggregation module (RDA) learns time-varying motion saliency, while the Region-aware Dynamic Excitation module (RDE) selectively enhances discriminative dynamic regions and suppresses interference-prone static ones—both learned end-to-end. Evaluated on multiple benchmark datasets, RDA-RDE achieves state-of-the-art performance, with particularly significant gains under challenging cross-view and occlusion scenarios involving carried objects.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Deep learning-based gait recognition has achieved great success in various applications. The key to accurate gait recognition lies in considering the unique and diverse behavior patterns in different motion regions, especially when covariates affect visual appearance. However, existing methods typically use predefined regions for temporal modeling, with fixed or equivalent temporal scales assigned to different types of regions, which makes it difficult to model motion regions that change dynamically over time and adapt to their specific patterns. To tackle this problem, we introduce a Region-aware Dynamic Aggregation and Excitation framework (GaitRDAE) that automatically searches for motion regions, assigns adaptive temporal scales and applies corresponding attention. Specifically, the framework includes two core modules: the Region-aware Dynamic Aggregation (RDA) module, which dynamically searches the optimal temporal receptive field for each region, and the Region-aware Dynamic Excitation (RDE) module, which emphasizes the learning of motion regions containing more stable behavior patterns while suppressing attention to static regions that are more susceptible to covariates. Experimental results show that GaitRDAE achieves state-of-the-art performance on several benchmark datasets.

Problem

• Dynamically modeling motion regions in gait recognition
• Adapting temporal scales for diverse behavior patterns
• Suppressing attention to static regions affected by covariates

Innovation

• Automatically searches for dynamic motion regions
• Assigns adaptive temporal scales per region
• Emphasizes stable patterns while suppressing static regions