Residual connection-based graph convolutional neural networks for gait recognition

Shopon,; Bari, A. S. M. Hossain; Gavrilova, Marina L.

doi:10.1007/s00371-021-02245-9

Cited by 10 publications

(4 citation statements)

References 45 publications

(53 reference statements)

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“…The spatial-temporal GCN models are widely used for sequence skeleton data based action recognition tasks [27], [29], [30], [31], [32], [33], [34], [35], [36]. Yan et al [27] firstly proposed the spatial-temporal GCN model (ST-GCN) for the action recognition.…”

Section: B Spatial-temporal Gcn For Sequential Learning Tasksmentioning

confidence: 99%

PedAST-GCN: Fast Pedestrian Crossing Intention Prediction Using Spatial–Temporal Attention Graph Convolution Networks

Ling,

Ma,

Zhang

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Accurately and timely predicting pedestrian crossing intentions in real-time is critical for operating intelligent vehicles on roads. Although existing models achieve promising accuracy using complex models and video image data, they are constrained for real-time practical use given the high model complexity, time-consuming data preprocessing, and low-quality image data in the wild. To address these, the paper proposes a Spatial-Temporal Attention Graph Convolution Network model for fast pedestrian crossing intention prediction (PedAST-GCN). It uses a lightweight GCN model as the backbone network with simple but robust graph representations of pedestrian crossing intention modality features, including pedestrian pose, bounding box, and vehicle speeds. The model is validated by comparing it with state-of-the-art models on two large-scale public datasets (JAAD and PIE). The results highlight the better performance of the PedAST-GCN model for pedestrian crossing intention prediction in terms of accuracy and computation times. The ablation analysis confirms the value of the backbone layer and graph design, the designed modality features, the effectiveness of attention mechanisms in capturing long-term dependencies (spatial-temporal attention) and fusing heterogeneous features (modality attention), and the robust performance across various observation lengths and in the presence of noisy data.

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Section: B Spatial-temporal Gcn For Sequential Learning Tasksmentioning

confidence: 99%

PedAST-GCN: Fast Pedestrian Crossing Intention Prediction Using Spatial–Temporal Attention Graph Convolution Networks

Ling,

Ma,

Zhang

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…The GCN-based models have been widely used for skeleton data-based recognition tasks, such as action recognition [16], [19], [20], [22], [23], [24], gait recognition [25], [26], hand gesture recognition [27], [28], etc. For example, Yan et al [16] proposed the spatio-temporal GCN model (ST-GCN) for action recognition using the natural connections of human body joints and three different strategies to design the adjacency matrix.…”

Section: A Skeleton Graph and Adjacency Matrix Designsmentioning

confidence: 99%

SA-BiGCN: Bi-Stream Graph Convolution Networks With Spatial Attentions for the Eye Contact Detection in the Wild

Ling,

Ma,

Xie

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Eye contact is essential in transmitting information and intention in the wild environment (e.g., urban streets or parking lots) with mixed vehicles and pedestrians. Compared with the vision image data, the human skeleton data are deemed to be robust to unconstrained surroundings and illumination. However, the skeleton graph-based approaches are mainly used for the action recognition. It is challenging to directly apply them to the eye detection task, which is momentary and dynamic given the complex wild environment. This paper proposes a Bi-stream Spatial Attention Graph Convolution Network (SA-BiGCN) for eye contact detection in the wild. We design a directed, nose-centric skeleton graph to capture relevant and hierarchical information and their interactions. We also propose a Bi-stream graph convolution network model with spatial attention to dynamically extract and fuse skeleton joints and bones information. The model was validated by comparing with state-of-art models on three large-scale public datasets, including JAAD, PIE, and LOOK. The results highlight the accuracy and generalization performance of the proposed SA-BiGCN model in detecting the eye contact in the wild environment. The ablation analysis validates the importance of the skeleton graph design, the spatial attention mechanism in the feature fusion process, as well as the model robustness against noisy skeleton data in terms of part occlusions, block occlusions, random occlusions, and random deviations.

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“…This can be problematic in scenarios where the data are noisy or incomplete. Another limitation is that GCNs may not perform well when dealing with large graphs, as the computation and memory requirements can become prohibitively high [ 44 , 129 ].…”

Section: Taxonomymentioning

confidence: 99%

“…At 2020 proposed partialRNN [ 39 ], GaitPart [ 40 ], 3DCNNGait [ 41 ], HMRGait [ 42 ], and GLN [ 43 ]. GCNGait [ 44 ], AT-GCN [ 45 ], 3DCNN [ 29 , 46 ] and UGaitNet [ 47 ] proposed at 2021. At 2022 proposed GCN + CNN [ 48 ], MVGait [ 49 ] and ViTGait [ 50 ].…”

Section: Figurementioning

confidence: 99%

Person Recognition Based on Deep Gait: A Survey

Khaliluzzaman

Uddin

Deb

et al. 2023

Sensors

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Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.

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Residual connection-based graph convolutional neural networks for gait recognition

Cited by 10 publications

References 45 publications

PedAST-GCN: Fast Pedestrian Crossing Intention Prediction Using Spatial–Temporal Attention Graph Convolution Networks

PedAST-GCN: Fast Pedestrian Crossing Intention Prediction Using Spatial–Temporal Attention Graph Convolution Networks

SA-BiGCN: Bi-Stream Graph Convolution Networks With Spatial Attentions for the Eye Contact Detection in the Wild

Person Recognition Based on Deep Gait: A Survey

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