TriGait: Aligning and Fusing Skeleton and Silhouette Gait Data via a Tri-Branch Network

• URL: http://arxiv.org/abs/2308.13340v1
• Date: Fri, 25 Aug 2023 12:19:51 GMT
• Title: TriGait: Aligning and Fusing Skeleton and Silhouette Gait Data via a Tri-Branch Network
• Authors: Yan Sun, Xueling Feng, Liyan Ma, Long Hu, Mark Nixon
• Abstract summary: Gait recognition is a promising biometric technology for identification due to its non-invasiveness and long-distance. external variations such as clothing changes and viewpoint differences pose significant challenges to gait recognition. A novel triple branch gait recognition framework, TriGait, is proposed in this paper.
• Score: 4.699718818019937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition is a promising biometric technology for identification due to its non-invasiveness and long-distance. However, external variations such as clothing changes and viewpoint differences pose significant challenges to gait recognition. Silhouette-based methods preserve body shape but neglect internal structure information, while skeleton-based methods preserve structure information but omit appearance. To fully exploit the complementary nature of the two modalities, a novel triple branch gait recognition framework, TriGait, is proposed in this paper. It effectively integrates features from the skeleton and silhouette data in a hybrid fusion manner, including a two-stream network to extract static and motion features from appearance, a simple yet effective module named JSA-TC to capture dependencies between all joints, and a third branch for cross-modal learning by aligning and fusing low-level features of two modalities. Experimental results demonstrate the superiority and effectiveness of TriGait for gait recognition. The proposed method achieves a mean rank-1 accuracy of 96.0% over all conditions on CASIA-B dataset and 94.3% accuracy for CL, significantly outperforming all the state-of-the-art methods. The source code will be available at https://github.com/feng-xueling/TriGait/.

Related papers

• GaitMA: Pose-guided Multi-modal Feature Fusion for Gait Recognition [26.721242606715354]
  Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. We propose a novel gait recognition framework, dubbed Gait Multi-model Aggregation Network (GaitMA) First, skeletons are represented by joint/limb-based heatmaps, and features from silhouettes and skeletons are respectively extracted using two CNN-based feature extractors.
  arXiv  Detail & Related papers  (2024-07-20T09:05:17Z)
• Learning-Based Biharmonic Augmentation for Point Cloud Classification [79.13962913099378]
  Biharmonic Augmentation (BA) is a novel and efficient data augmentation technique. BA diversifies point cloud data by imposing smooth non-rigid deformations on existing 3D structures. We present AdvTune, an advanced online augmentation system that integrates adversarial training.
  arXiv  Detail & Related papers  (2023-11-10T14:04:49Z)
• Distillation-guided Representation Learning for Unconstrained Gait Recognition [50.0533243584942]
  We propose a framework, termed GAit DEtection and Recognition (GADER), for human authentication in challenging outdoor scenarios. GADER builds discriminative features through a novel gait recognition method, where only frames containing gait information are used. We evaluate our method on multiple State-of-The-Arts(SoTA) gait baselines and demonstrate consistent improvements on indoor and outdoor datasets.
  arXiv  Detail & Related papers  (2023-07-27T01:53:57Z)
• Multi-Modal Human Authentication Using Silhouettes, Gait and RGB [59.46083527510924]
  Whole-body-based human authentication is a promising approach for remote biometrics scenarios. We propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis.
  arXiv  Detail & Related papers  (2022-10-08T15:17:32Z)
• Towards a Deeper Understanding of Skeleton-based Gait Recognition [4.812321790984493]
  In recent years, most gait recognition methods used the person's silhouette to extract the gait features. Model-based methods do not suffer from these problems and are able to represent the temporal motion of body joints. In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks.
  arXiv  Detail & Related papers  (2022-04-16T18:23:37Z)
• Spatio-temporal Gait Feature with Adaptive Distance Alignment [90.5842782685509]
  We try to increase the difference of gait features of different subjects from two aspects: the optimization of network structure and the refinement of extracted gait features. Our method is proposed, it consists of Spatio-temporal Feature Extraction (SFE) and Adaptive Distance Alignment (ADA) ADA uses a large number of unlabeled gait data in real life as a benchmark to refine the extracted-temporal features to make them have low inter-class similarity and high intra-class similarity.
  arXiv  Detail & Related papers  (2022-03-07T13:34:00Z)
• Combining the Silhouette and Skeleton Data for Gait Recognition [13.345465199699]
  Two dominant gait recognition works are appearance-based and model-based, which extract features from silhouettes and skeletons, respectively. This paper proposes a CNN-based branch taking silhouettes as input and a GCN-based branch taking skeletons as input. For better gait representation in the GCN-based branch, we present a fully connected graph convolution operator to integrate multi-scale graph convolutions.
  arXiv  Detail & Related papers  (2022-02-22T03:21:51Z)
• Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes [20.766540020533803]
  This paper proposes a simple yet effective bimodal fusion network, which mines the complementary clues of skeletons and silhouettes to learn rich features for gait identification. Under the most challenging condition of walking in different clothes on CASIA-B, our method achieves the rank-1 accuracy of 92.1%.
  arXiv  Detail & Related papers  (2021-10-26T04:42:24Z)
• G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation [49.421099172544196]
  We propose a novel semantic-guided feature imitation technique, which automatically performs soft matching between feature pairs across all pyramid levels. We also introduce contrastive distillation to effectively capture the information encoded in the relationship between different feature regions. Our method consistently outperforms the existing detection KD techniques, and works when (1) components in the framework are used separately and in conjunction.
  arXiv  Detail & Related papers  (2021-08-17T07:44:27Z)
• JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping [6.851535012702575]
  We research on using 2D joints to recognize gait in this paper. JointsGait is put forward to extract gait information from 2D human body joints.
  arXiv  Detail & Related papers  (2020-04-27T08:30:37Z)
• Cross-modality Person re-identification with Shared-Specific Feature Transfer [112.60513494602337]
  Cross-modality person re-identification (cm-ReID) is a challenging but key technology for intelligent video analysis. We propose a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics.
  arXiv  Detail & Related papers  (2020-02-28T00:18:45Z)

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.