Local Spatiotemporal Convolutional Network for Robust Gait Recognition

• URL: http://arxiv.org/abs/2605.14548v1
• Date: Thu, 14 May 2026 08:28:49 GMT
• Title: Local Spatiotemporal Convolutional Network for Robust Gait Recognition
• Abstract summary: Gait recognition is a promising biometric technology that identifies individuals through their unique walking patterns.<n>Existing approaches rely on either static appearance features extracted from individual silhouette frames or employ complex sequential models.<n>We propose a Local Stemporal Convolutional Network (LSTCN), a structurally simple yet highly effective dual-branch architecture that endows standard two-dimensional convolutional networks with the capacity to extract temporal information.
• Score: 7.0676119183443395
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Gait recognition, as a promising biometric technology, identifies individuals through their unique walking patterns and offers distinctive advantages including non-invasiveness, long-range applicability, and resistance to deliberate disguise. Despite these merits, capturing the intrinsic motion patterns concealed within consecutive video frames remains challenging due to the complexity of video data and the interference of external covariates such as viewpoint changes, clothing variations, and carrying conditions. Existing approaches predominantly rely on either static appearance features extracted from individual silhouette frames or employ complex sequential models (\eg, LSTM, 3D convolutions) that demand substantial computational resources and sophisticated training strategies. To address these limitations, we propose a Local Spatiotemporal Convolutional Network (LSTCN), a structurally simple yet highly effective dual-branch architecture that endows standard two-dimensional convolutional networks with the capacity to extract temporal information. Specifically, we introduce a Global Bidirectional Spatial Pooling (GBSP) mechanism that reduces the dimensionality of gait tensors by decomposing spatial features into horizontal and vertical strip-based local representations, enabling the temporal dimension to participate in standard 2D convolution operations. Building upon this, we design a Local Spatiotemporal Convolutional (LSTC) layer that jointly processes temporal and spatial dimensions, allowing the network to adaptively learn strip-based gait motion patterns. We further extend this formulation with asymmetric convolution kernels that independently attend to the temporal, spatial, and joint spatiotemporal domains, thereby enriching the extracted feature representations.

Related papers

• Towards Physically Consistent 4D Scene Reconstruction for Closed-loop Autonomous Driving Simulation [19.055975606481635]
  High-fidelity street scene reconstruction is pivotal for end-to-end autonomous driving simulation.<n>Existing 3DGS methods and their 4D extensions fail to simultaneously achieve both.<n>We propose Orthogonal Projected Gradient (OPG) to restore spatial identifiability.
  arXiv  Detail & Related papers  (2026-05-20T11:09:53Z)
• Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression [97.66080040613726]
  We propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space.<n>Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations.<n>We show Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime.
  arXiv  Detail & Related papers  (2025-09-18T03:51:06Z)
• Deformable Dynamic Convolution for Accurate yet Efficient Spatio-Temporal Traffic Prediction [1.7268829007643391]
  Deformable Dynamic Convolutional Network (DDCN) is a novel CNN-based architecture that integrates both deformable and receptive dynamic operations.<n> DDCN achieves competitive predictive performance while significantly reducing computational costs, underscoring its potential for large-scale and real-time deployment.
  arXiv  Detail & Related papers  (2025-07-13T06:49:35Z)
• Spline Deformation Field [21.755382164519776]
  inductive biases can hinder canonical spatial coherence in ill-posed scenarios.<n>We introduce a novel low-rank spatial encoding, replacing conventional coupled techniques.<n>It achieves competitive dynamic reconstruction quality compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-10T08:11:46Z)
• Linear Attention is Enough in Spatial-Temporal Forecasting [0.0]
  We propose treating nodes in road networks at different time steps as independent spatial-temporal tokens. We then feed them into a vanilla Transformer to learn complex spatial-temporal patterns. Our code achieves state-of-the-art performance at an affordable computational cost.
  arXiv  Detail & Related papers  (2024-08-17T10:06:50Z)
• Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition [7.682613953680041]
  We propose the Surgical Transformer (Surgformer) to address the issues of spatial-temporal modeling and redundancy in an end-to-end manner. We show that our proposed Surgformer performs favorably against the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-07T16:16:31Z)
• Gait Recognition in the Wild with Multi-hop Temporal Switch [81.35245014397759]
  gait recognition in the wild is a more practical problem that has attracted the attention of the community of multimedia and computer vision. This paper presents a novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes.
  arXiv  Detail & Related papers  (2022-09-01T10:46:09Z)
• Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition [62.46544616232238]
  Previous motion recognition methods have achieved promising performance through the tightly coupled multi-temporal representation. We propose to decouple and recouple caused caused representation for RGB-D-based motion recognition.
  arXiv  Detail & Related papers  (2021-12-16T18:59:47Z)
• Spatio-Temporal Representation Factorization for Video-based Person Re-Identification [55.01276167336187]
  We propose Spatio-Temporal Representation Factorization module (STRF) for re-ID. STRF is a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID. We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-07-25T19:29:37Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• Multi-Temporal Convolutions for Human Action Recognition in Videos [83.43682368129072]
  We present a novel temporal-temporal convolution block that is capable of extracting at multiple resolutions. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture.
  arXiv  Detail & Related papers  (2020-11-08T10:40:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.