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Exploring Deep Models for Practical Gait Recognition
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Gait recognition is a rapidly advancing vision technique for person identification from a distance. Prior studies predominantly employed relatively shallow networks to extract subtle gait features, achieving impressive successes in constrained settings. Nevertheless, experiments revealed that existing methods mostly produce unsatisfactory results when applied to newly released real-world gait datasets. This paper presents a unified perspective to explore how to construct deep models for state-of-the-art outdoor gait recognition, including the classical CNN-based and emerging Transformer-based architectures. Specifically, we challenge the stereotype of shallow gait models and demonstrate the superiority of explicit temporal modeling and deep transformer structure for discriminative gait representation learning. Consequently, the proposed CNN-based DeepGaitV2 series and Transformer-based SwinGait series exhibit significant performance improvements on Gait3D and GREW. As for the constrained gait datasets, the DeepGaitV2 series also reaches a new state-of-the-art in most cases, convincingly showing its practicality and generality. The source code is available at https://github.com/ShiqiYu/OpenGait.
Forward citations
Cited by 8 Pith papers
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Probing Identity-Specific Motion Signatures: A Controlled Diagnostic Study
Identity-specific free-throw motion signatures exist and are learnable by video models, but models prefer static appearance shortcuts unless silhouettes or skeletons suppress them.
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Decoding Children's Gait Behavior
A new 1,185-video pediatric gait dataset with EVGS labels, plus a VideoMAE-based model, reaches ~84% average accuracy on 34 clinical gait items — far above MLLMs and prior gait models.
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GaitFace: A Multimodal Dataset for Long-Range Person Identification
A 70-subject public long-range face+gait dataset and protocols show SOTA models collapse on native low-res and elevated 100 m probes despite strong optical-zoom performance.
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DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Combining silhouettes with monocular depth estimates from the same RGB frames, fused through a multi-scale cross-level attention network, raises gait recognition rank-1 accuracy on CCPG, SUSTech1K, and CASIA-B.
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On Denoising Walking Videos for Gait Recognition
DenoisingGait combines frozen Stable Diffusion features with learned direction-vector matching to create Gait Feature Fields, reporting new state-of-the-art rank-1 accuracy on CCPG and most settings of CASIA-B*, SUSTech1K.
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BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models
Combining features from intermediate layers of large vision models improves gait recognition accuracy, and the proposed BiggerGait baseline achieves state-of-the-art results on CCPG and cross-domain benchmarks.
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PCA-Guided Autoencoding for Structured Dimensionality Reduction in Active Infrared Thermography
A PCA-guided autoencoder with a distillation loss is claimed to give a more structured latent space and better defect characterization than existing dimensionality-reduction methods in active infrared thermography.
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Mind the Gap: Bridging Occlusion in Gait Recognition via Residual Gap Correction
RG-Gait learns a residual correction to gait features for occluded inputs, adaptively weighted by an occlusion module, improving occluded recognition while retaining holistic performance.
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