Simple and efficient pose-based gait recognition method for challenging environments

Lima, Vitor de; Melo, Victor Hugo Cunha de; Schwartz, William Robson

doi:10.1007/s10044-020-00935-z

Cited by 15 publications

(6 citation statements)

References 32 publications

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“…The proposed methods necessitate a wide range of images to estimate posture skeletons, which negates the benefit of most datasets because they only provide silhouettes. As a result, [8] conducted tests on two publicly accessible datasets that provide Red, Green, and Blue (RGB) images, the CASIA Dataset A and the CASIA Dataset B. The proposed approach focuses on gait analysis in light of posture.…”

Section: Related Workmentioning

confidence: 99%

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

Vasudevan¹,

Mattins²,

Srivarshan³

et al. 2023

Computers, Materials &Amp; Continua

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Gait refers to a person's particular movements and stance while moving around. Although each person's gait is unique and made up of a variety of tiny limb orientations and body positions, they all have common characteristics that help to define normalcy. Swiftly identifying such characteristics that are difficult to spot by the naked eye, can help in monitoring the elderly who require constant care and support. Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait. It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis. Gait images made publicly available by the Chinese Academy of Sciences (CASIA) Gait Database was used in this study. After evaluating using the CASIA B and C datasets, this paper proposes a Convolutional Neural Network (CNN) and a CNN Long Short-Term Memory Network (CNN-LSTM) model for classifying the gait silhouette images. Transfer learning models such as MobileNetV2, InceptionV3, Visual Geometry Group (VGG) networks such as VGG16 and VGG19, Residual Networks (ResNet) like the ResNet9 and ResNet50, were used to compare the efficacy of the proposed models. CNN proved to be the best by achieving the highest accuracy of 94.29%. This was followed by ResNet9 and CNN-LSTM, which arrived at 93.30% and 87.25% accuracy, respectively.

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Section: Related Workmentioning

confidence: 99%

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

Vasudevan¹,

Mattins²,

Srivarshan³

et al. 2023

Computers, Materials &Amp; Continua

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“…Handcrafted methods in gait recognition can be divided into two categories: modelbased and model-free. The model-based approach uses a human model consisting of stick figures and joints to extract motion information [5][6][7][8]. In the work of Deng et al (2018) [9], a deterministic learning algorithm was used to encode both spatial-temporal features and kinematic features.…”

Section: Handcrafted Approachmentioning

confidence: 99%

Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer

Mogan

Lee

Lim

et al. 2023

Sensors

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Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address these challenges, this paper proposes a multi-model gait recognition system that integrates Convolutional Neural Networks (CNNs) and Vision Transformer. The first step in the process is to obtain a gait energy image, which is achieved by applying an averaging technique to a gait cycle. The gait energy image is then fed into three different models, DenseNet-201, VGG-16, and a Vision Transformer. These models are pre-trained and fine-tuned to encode the salient gait features that are specific to an individual’s walking style. Each model provides prediction scores for the classes based on the encoded features, and these scores are then summed and averaged to produce the final class label. The performance of this multi-model gait recognition system was evaluated on three datasets, CASIA-B, OU-ISIR dataset D, and OU-ISIR Large Population dataset. The experimental results showed substantial improvement compared to existing methods on all three datasets. The integration of CNNs and ViT allows the system to learn both the pre-defined and distinct features, providing a robust solution for gait recognition even under the influence of covariates.

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“…On their self-collected gait datasets, the method obtained an average recognition rate of 89.26%. Lima et al (2021) [ 26 ] used PoseDist and PoseFrame for gait recognition problems. The coordinates of each subject were identified using pose estimation.…”

Section: Related Workmentioning

confidence: 99%

Gait-ViT: Gait Recognition with Vision Transformer

Mogan

Lee

Lim

et al. 2022

Sensors

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Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its advantages, such as being perceivable at a long distance and difficult to replicate. The existing works mostly leverage Convolutional Neural Networks for gait recognition. The Convolutional Neural Networks perform well in image recognition tasks; however, they lack the attention mechanism to emphasize more on the significant regions of the image. The attention mechanism encodes information in the image patches, which facilitates the model to learn the substantial features in the specific regions. In light of this, this work employs the Vision Transformer (ViT) with an attention mechanism for gait recognition, referred to as Gait-ViT. In the proposed Gait-ViT, the gait energy image is first obtained by averaging the series of images over the gait cycle. The images are then split into patches and transformed into sequences by flattening and patch embedding. Position embedding, along with patch embedding, are applied on the sequence of patches to restore the positional information of the patches. Subsequently, the sequence of vectors is fed to the Transformer encoder to produce the final gait representation. As for the classification, the first element of the sequence is sent to the multi-layer perceptron to predict the class label. The proposed method obtained 99.93% on CASIA-B, 100% on OU-ISIR D and 99.51% on OU-LP, which exhibit the ability of the Vision Transformer model to outperform the state-of-the-art methods.

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Simple and efficient pose-based gait recognition method for challenging environments

Cited by 15 publications

References 32 publications

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer

Gait-ViT: Gait Recognition with Vision Transformer

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